Update Face Filter

- Remove old face filter - plugins.extract.pipeline: Expose plugins directly - Change `is_aligned` from plugin level to ExtractMedia level - Allow extract pipeline to take faceswap aligned images - Add ability for recognition plugins to accept aligned faces as input - Add face filter to recognition plugin - Move extractor pipeline IO ops to own class
2026-01-15 12:15:15 +00:00 · 2022-10-31 18:25:32 +00:00
parent d1a7f7a8bc
commit 1d1face00d
19 changed files with 1187 additions and 826 deletions
--- a/lib/align/alignments.py
+++ b/lib/align/alignments.py
@@ -68,6 +68,7 @@ class PNGHeaderSourceDict(TypedDict):
    face_index: int
    source_filename: str
    source_is_video: bool
+    source_frame_dims: Optional[Tuple[int, int]]


 class AlignmentDict(TypedDict):
--- a/lib/align/detected_face.py
+++ b/lib/align/detected_face.py
@@ -1079,7 +1079,8 @@ def update_legacy_png_header(filename: str, alignments: Alignments
                            original_filename=orig_filename,
                            face_index=face_idx,
                            source_filename=src_fname,
-                            source_is_video=False))  # Can't check so set false
+                            source_is_video=False,  # Can't check so set false
+                            source_frame_dims=None))

    out_filename = f"{os.path.splitext(filename)[0]}.png"  # Make sure saved file is png
    out_image = encode_image(in_image, ".png", metadata=meta)
--- a/lib/cli/args.py
+++ b/lib/cli/args.py
@@ -521,11 +521,10 @@ class ExtractArgs(ExtractConvertArgs):
            default=None,
            nargs="+",
            group=_("Face Processing"),
-            help=_("Optionally filter out people who you do not wish to process by passing in an "
-                   "image of that person. Should be a front portrait with a single person in the "
-                   "image. Multiple images can be added space separated. NB: Using face filter "
-                   "will significantly decrease extraction speed and its accuracy cannot be "
-                   "guaranteed.")))
+            help=_("Optionally filter out people who you do not wish to extract by passing in "
+                   "images of those people. Should be a small variety of images at different "
+                   "angles and in different conditions. Multiple images can be added space "
+                   "separated.")))
        argument_list.append(dict(
            opts=("-f", "--filter"),
            action=FilesFullPaths,
@@ -534,11 +533,10 @@ class ExtractArgs(ExtractConvertArgs):
            default=None,
            nargs="+",
            group=_("Face Processing"),
-            help=_("Optionally select people you wish to process by passing in an image of that "
-                   "person. Should be a front portrait with a single person in the image. "
-                   "Multiple images can be added space separated. NB: Using face filter will "
-                   "significantly decrease extraction speed and its accuracy cannot be "
-                   "guaranteed.")))
+            help=_("Optionally select people you wish to extract by passing in images of that "
+                   "person. Should be a small variety of images at different angles and in "
+                   "different conditions. Multiple identities can be filtered. Multiple images "
+                   "can be added space separated.")))
        argument_list.append(dict(
            opts=("-l", "--ref_threshold"),
            action=Slider,
@@ -546,12 +544,10 @@ class ExtractArgs(ExtractConvertArgs):
            rounding=2,
            type=float,
            dest="ref_threshold",
-            default=0.4,
+            default=0.65,
            group=_("Face Processing"),
            help=_("For use with the optional nfilter/filter files. Threshold for positive face "
-                   "recognition. Lower values are stricter. NB: Using face filter will "
-                   "significantly decrease extraction speed and its accuracy cannot be "
-                   "guaranteed.")))
+                   "recognition. Higher values are stricter.")))
        argument_list.append(dict(
            opts=("-sz", "--size"),
            action=Slider,
--- a/lib/face_filter.py
+++ b/lib/face_filter.py
@@ -1,181 +0,0 @@
-#!/usr/bin python3
-""" Face Filterer for extraction in faceswap.py """
-
-import logging
-
-from lib.align import AlignedFace
-from lib.vgg_face import VGGFace
-from lib.image import read_image
-from plugins.extract.pipeline import Extractor, ExtractMedia
-
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
-
-def avg(arr):
-    """ Return an average """
-    return sum(arr) * 1.0 / len(arr)
-
-
-class FaceFilter():
-    """ Face filter for extraction
-        NB: we take only first face, so the reference file should only contain one face. """
-
-    def __init__(self, reference_file_paths, nreference_file_paths, detector, aligner,
-                 multiprocess=False, threshold=0.4):
-        logger.debug("Initializing %s: (reference_file_paths: %s, nreference_file_paths: %s, "
-                     "detector: %s, aligner: %s, multiprocess: %s, threshold: %s)",
-                     self.__class__.__name__, reference_file_paths, nreference_file_paths,
-                     detector, aligner, multiprocess, threshold)
-        self.vgg_face = VGGFace()
-        self.filters = self.load_images(reference_file_paths, nreference_file_paths)
-        # TODO Revert face-filter to use the selected detector and aligner.
-        # Currently Tensorflow does not release vram after it has been allocated
-        # Whilst this vram can still be used, the pipeline for the extraction process can't see
-        # it so thinks there is not enough vram available.
-        # Either the pipeline will need to be changed to be re-usable by face-filter and extraction
-        # Or another vram measurement technique will need to be implemented to for when tensorflow
-        # has already performed allocation. For now we force CPU detectors.
-
-        # self.align_faces(detector, aligner, multiprocess)
-        self.align_faces("cv2-dnn", "cv2-dnn", "none", multiprocess)
-
-        self.get_filter_encodings()
-        self.threshold = threshold
-        logger.debug("Initialized %s", self.__class__.__name__)
-
-    @staticmethod
-    def load_images(reference_file_paths, nreference_file_paths):
-        """ Load the images """
-        retval = dict()
-        for fpath in reference_file_paths:
-            retval[fpath] = {"image": read_image(fpath, raise_error=True),
-                             "type": "filter"}
-        for fpath in nreference_file_paths:
-            retval[fpath] = {"image": read_image(fpath, raise_error=True),
-                             "type": "nfilter"}
-        logger.debug("Loaded filter images: %s", {k: v["type"] for k, v in retval.items()})
-        return retval
-
-    # Extraction pipeline
-    def align_faces(self, detector_name, aligner_name, masker_name, multiprocess):
-        """ Use the requested detectors to retrieve landmarks for filter images """
-        extractor = Extractor(detector_name,
-                              aligner_name,
-                              masker_name,
-                              multiprocess=multiprocess)
-        self.run_extractor(extractor)
-        del extractor
-        self.load_aligned_face()
-
-    def run_extractor(self, extractor):
-        """ Run extractor to get faces """
-        for _ in range(extractor.passes):
-            extractor.launch()
-            self.queue_images(extractor)
-            for faces in extractor.detected_faces():
-                filename = faces.filename
-                detected_faces = faces.detected_faces
-                if len(detected_faces) > 1:
-                    logger.warning("Multiple faces found in %s file: '%s'. Using first detected "
-                                   "face.", self.filters[filename]["type"], filename)
-                self.filters[filename]["detected_face"] = detected_faces[0]
-
-    def queue_images(self, extractor):
-        """ queue images for detection and alignment """
-        in_queue = extractor.input_queue
-        for fname, img in self.filters.items():
-            logger.debug("Adding to filter queue: '%s' (%s)", fname, img["type"])
-            feed_dict = ExtractMedia(fname, img["image"], detected_faces=img.get("detected_faces"))
-            logger.debug("Queueing filename: '%s' items: %s", fname, feed_dict)
-            in_queue.put(feed_dict)
-        logger.debug("Sending EOF to filter queue")
-        in_queue.put("EOF")
-
-    def load_aligned_face(self):
-        """ Align the faces for vgg_face input """
-        for filename, face in self.filters.items():
-            logger.debug("Loading aligned face: '%s'", filename)
-            image = face["image"]
-            detected_face = face["detected_face"]
-            detected_face.load_aligned(image, centering="legacy", size=224)
-            face["face"] = detected_face.aligned.face
-            del face["image"]
-            logger.debug("Loaded aligned face: ('%s', shape: %s)",
-                         filename, face["face"].shape)
-
-    def get_filter_encodings(self):
-        """ Return filter face encodings from Keras VGG Face """
-        for filename, face in self.filters.items():
-            logger.debug("Getting encodings for: '%s'", filename)
-            encodings = self.vgg_face.predict(face["face"])
-            logger.debug("Filter Filename: %s, encoding shape: %s", filename, encodings.shape)
-            face["encoding"] = encodings
-            del face["face"]
-
-    def check(self, image, detected_face):
-        """ Check the extracted Face
-
-        Parameters
-        ----------
-        image: :class:`numpy.ndarray`
-            The original frame that contains the face to be checked
-        detected_face: :class:`lib.align.DetectedFace`
-            The detected face object that contains the face to be checked
-
-        Returns
-        -------
-        bool
-            ``True`` if the face matches a filter otherwise ``False``
-        """
-        logger.trace("Checking face with FaceFilter")
-        distances = {"filter": list(), "nfilter": list()}
-        feed = AlignedFace(detected_face.landmarks_xy, image=image, size=224, centering="legacy")
-        encodings = self.vgg_face.predict(feed.face)
-        for filt in self.filters.values():
-            similarity = self.vgg_face.find_cosine_similiarity(filt["encoding"], encodings)
-            distances[filt["type"]].append(similarity)
-
-        avgs = {key: avg(val) if val else None for key, val in distances.items()}
-        mins = {key: min(val) if val else None for key, val in distances.items()}
-        # Filter
-        if distances["filter"] and avgs["filter"] > self.threshold:
-            msg = "Rejecting filter face: {} > {}".format(round(avgs["filter"], 2), self.threshold)
-            retval = False
-        # nFilter no Filter
-        elif not distances["filter"] and avgs["nfilter"] < self.threshold:
-            msg = "Rejecting nFilter face: {} < {}".format(round(avgs["nfilter"], 2),
-                                                           self.threshold)
-            retval = False
-        # Filter with nFilter
-        elif distances["filter"] and distances["nfilter"] and mins["filter"] > mins["nfilter"]:
-            msg = ("Rejecting face as distance from nfilter sample is smaller: (filter: {}, "
-                   "nfilter: {})".format(round(mins["filter"], 2), round(mins["nfilter"], 2)))
-            retval = False
-        elif distances["filter"] and distances["nfilter"] and avgs["filter"] > avgs["nfilter"]:
-            msg = ("Rejecting face as average distance from nfilter sample is smaller: (filter: "
-                   "{}, nfilter: {})".format(round(mins["filter"], 2), round(mins["nfilter"], 2)))
-            retval = False
-        elif distances["filter"] and distances["nfilter"]:
-            # k-nearest-neighbor classifier
-            var_k = min(5, min(len(distances["filter"]), len(distances["nfilter"])) + 1)
-            var_n = sum(list(map(lambda x: x[0],
-                                 list(sorted([(1, d) for d in distances["filter"]] +
-                                             [(0, d) for d in distances["nfilter"]],
-                                             key=lambda x: x[1]))[:var_k])))
-            ratio = var_n/var_k
-            if ratio < 0.5:
-                msg = ("Rejecting face as k-nearest neighbors classification is less than "
-                       "0.5: {}".format(round(ratio, 2)))
-                retval = False
-            else:
-                msg = None
-                retval = True
-        else:
-            msg = None
-            retval = True
-        if msg:
-            logger.verbose(msg)
-        else:
-            logger.trace("Accepted face: (similarity: %s, threshold: %s)",
-                         distances, self.threshold)
-        return retval
--- a/lib/image.py
+++ b/lib/image.py
@@ -11,7 +11,7 @@ import sys
 from ast import literal_eval
 from bisect import bisect
 from concurrent import futures
-from typing import Optional
+from typing import Optional, TYPE_CHECKING, Union
 from zlib import crc32

 import cv2
@@ -24,6 +24,9 @@ from lib.multithreading import MultiThread
 from lib.queue_manager import queue_manager, QueueEmpty
 from lib.utils import convert_to_secs, FaceswapError, _video_extensions, get_image_paths

+if TYPE_CHECKING:
+    from lib.align.alignments import PNGHeaderDict
+
 logger = logging.getLogger(__name__)  # pylint:disable=invalid-name

 # ################### #
@@ -552,7 +555,9 @@ def update_existing_metadata(filename, metadata):
    os.replace(tmp_filename, filename)


-def encode_image(image, extension, metadata=None):
+def encode_image(image: np.ndarray,
+                 extension: str,
+                 metadata: Optional["PNGHeaderDict"] = None) -> bytes:
    """ Encode an image.

    Parameters
@@ -580,7 +585,7 @@ def encode_image(image, extension, metadata=None):
        raise ValueError("Metadata is only supported for .png images")
    retval = cv2.imencode(extension, image)[1]
    if metadata:
-        retval = np.frombuffer(png_write_meta(retval.tobytes(), metadata), dtype="uint8")
+        retval = png_write_meta(retval.tobytes(), metadata)
    return retval


@@ -1032,7 +1037,7 @@ class ImagesLoader(ImageIO):
            If the given location is a file and does not have a valid video extension.

        """
-        if os.path.isdir(self.location):
+        if not isinstance(self.location, str) or os.path.isdir(self.location):
            retval = False
        elif os.path.splitext(self.location)[1].lower() in _video_extensions:
            retval = True
@@ -1423,7 +1428,10 @@ class ImagesSaver(ImageIO):
            executor.submit(self._save, *item)
        executor.shutdown()

-    def _save(self, filename: str, image: bytes, sub_folder: Optional[str]) -> None:
+    def _save(self,
+              filename: str,
+              image: Union[bytes, np.ndarray],
+              sub_folder: Optional[str]) -> None:
        """ Save a single image inside a ThreadPoolExecutor

        Parameters
@@ -1431,8 +1439,8 @@ class ImagesSaver(ImageIO):
        filename: str
            The filename of the image to be saved. NB: Any folders passed in with the filename
            will be stripped and replaced with :attr:`location`.
-        image: bytes
-            The encoded image to be saved
+        image: bytes or :class:`numpy.ndarray`
+            The encoded image or numpy array to be saved
        subfolder: str or ``None``
            If the file should be saved in a subfolder in the output location, the subfolder should
            be provided here. ``None`` for no subfolder.
@@ -1444,15 +1452,19 @@ class ImagesSaver(ImageIO):
        filename = os.path.join(location, os.path.basename(filename))
        try:
            if self._as_bytes:
+                assert isinstance(image, bytes)
                with open(filename, "wb") as out_file:
                    out_file.write(image)
            else:
                cv2.imwrite(filename, image)
            logger.trace("Saved image: '%s'", filename)  # type:ignore
        except Exception as err:  # pylint: disable=broad-except
-            logger.error("Failed to save image '%s'. Original Error: %s", filename, err)
+            logger.error("Failed to save image '%s'. Original Error: %s", filename, str(err))

-    def save(self, filename: str, image: bytes, sub_folder: Optional[str] = None) -> None:
+    def save(self,
+             filename: str,
+             image: Union[bytes, np.ndarray],
+             sub_folder: Optional[str] = None) -> None:
        """ Save the given image in the background thread

        Ensure that :func:`close` is called once all save operations are complete.
--- a/locales/es/LC_MESSAGES/lib.cli.args.mo
+++ b/locales/es/LC_MESSAGES/lib.cli.args.mo
--- a/locales/es/LC_MESSAGES/lib.cli.args.po
+++ b/locales/es/LC_MESSAGES/lib.cli.args.po
@@ -6,8 +6,8 @@ msgid ""
 msgstr ""
 "Project-Id-Version: faceswap.spanish\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2022-10-10 13:05+0100\n"
-"PO-Revision-Date: 2022-10-10 13:07+0100\n"
+"POT-Creation-Date: 2022-10-31 11:51+0000\n"
+"PO-Revision-Date: 2022-10-31 11:53+0000\n"
 "Last-Translator: \n"
 "Language-Team: tokafondo\n"
 "Language: es\n"
@@ -55,7 +55,7 @@ msgstr ""
 "almacenarlo en la carpeta pde instalación de faceswap"

 #: lib/cli/args.py:320 lib/cli/args.py:329 lib/cli/args.py:337
-#: lib/cli/args.py:386 lib/cli/args.py:672 lib/cli/args.py:681
+#: lib/cli/args.py:386 lib/cli/args.py:668 lib/cli/args.py:677
 msgid "Data"
 msgstr "Datos"

@@ -102,8 +102,8 @@ msgstr ""

 #: lib/cli/args.py:396 lib/cli/args.py:412 lib/cli/args.py:424
 #: lib/cli/args.py:463 lib/cli/args.py:481 lib/cli/args.py:493
-#: lib/cli/args.py:502 lib/cli/args.py:691 lib/cli/args.py:718
-#: lib/cli/args.py:756
+#: lib/cli/args.py:502 lib/cli/args.py:687 lib/cli/args.py:714
+#: lib/cli/args.py:752
 msgid "Plugins"
 msgstr "Extensiones"

@@ -272,9 +272,9 @@ msgstr ""
 "Obtenga y almacene codificaciones de identidad facial de VGGFace2. Ralentiza "
 "un poco la extracción, pero ahorrará tiempo si usa 'sort by face'"

-#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:536
-#: lib/cli/args.py:550 lib/cli/args.py:793 lib/cli/args.py:807
-#: lib/cli/args.py:820 lib/cli/args.py:834
+#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:535
+#: lib/cli/args.py:548 lib/cli/args.py:789 lib/cli/args.py:803
+#: lib/cli/args.py:816 lib/cli/args.py:830
 msgid "Face Processing"
 msgstr "Proceso de Caras"

@@ -287,52 +287,44 @@ msgstr ""
 "a lo largo de la diagonal del cuadro delimitador. Establecer a 0 para "
 "desactivar"

-#: lib/cli/args.py:524 lib/cli/args.py:808
+#: lib/cli/args.py:524
 msgid ""
-"Optionally filter out people who you do not wish to process by passing in an "
-"image of that person. Should be a front portrait with a single person in the "
-"image. Multiple images can be added space separated. NB: Using face filter "
-"will significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally filter out people who you do not wish to extract by passing in "
+"images of those people. Should be a small variety of images at different "
+"angles and in different conditions. Multiple images can be added space "
+"separated."
 msgstr ""
-"Opcionalmente, puede filtrar las personas que no desea procesar pasando una "
-"imagen de esa persona. Debe ser un retrato frontal con una sola persona en "
-"la imagen. Se pueden añadir varias imágenes separadas por espacios. NB: El "
-"uso del filtro de caras disminuirá significativamente la velocidad de "
-"extracción y no se puede garantizar su precisión."
+"Opcionalmente, filtre a las personas que no desea extraer pasando imágenes "
+"de esas personas. Debe ser una pequeña variedad de imágenes en diferentes "
+"ángulos y en diferentes condiciones. Se pueden agregar varias imágenes "
+"separadas por espacios."

-#: lib/cli/args.py:537 lib/cli/args.py:821
+#: lib/cli/args.py:536
 msgid ""
-"Optionally select people you wish to process by passing in an image of that "
-"person. Should be a front portrait with a single person in the image. "
-"Multiple images can be added space separated. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally select people you wish to extract by passing in images of that "
+"person. Should be a small variety of images at different angles and in "
+"different conditions. Multiple identities can be filtered. Multiple images "
+"can be added space separated."
 msgstr ""
-"Opcionalmente, seleccione las personas que desea procesar pasando una imagen "
-"de esa persona. Debe ser un retrato frontal con una sola persona en la "
-"imagen. Se pueden añadir varias imágenes separadas por espacios. NB: El uso "
-"del filtro facial disminuirá significativamente la velocidad de extracción y "
-"no se puede garantizar su precisión."
+"Opcionalmente, seleccione las personas que desea extraer pasando imágenes de "
+"esa persona. Debe ser una pequeña variedad de imágenes en diferentes ángulos "
+"y en diferentes condiciones. Se pueden filtrar múltiples identidades. Se "
+"pueden agregar varias imágenes separadas por espacios."

-#: lib/cli/args.py:551 lib/cli/args.py:835
+#: lib/cli/args.py:549
 msgid ""
 "For use with the optional nfilter/filter files. Threshold for positive face "
-"recognition. Lower values are stricter. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"recognition. Higher values are stricter."
 msgstr ""
-"Para usar con los archivos opcionales nfilter/filter. Umbral para el "
-"reconocimiento positivo de caras. Los valores más bajos son más estrictos. "
-"NB: El uso del filtro facial disminuirá significativamente la velocidad de "
-"extracción y no se puede garantizar su precisión."
+"Para usar con los archivos nfilter/filter opcionales. Umbral para el "
+"reconocimiento facial positivo. Los valores más altos son más estrictos."

-#: lib/cli/args.py:562 lib/cli/args.py:574 lib/cli/args.py:586
-#: lib/cli/args.py:598
+#: lib/cli/args.py:558 lib/cli/args.py:570 lib/cli/args.py:582
+#: lib/cli/args.py:594
 msgid "output"
 msgstr "salida"

-#: lib/cli/args.py:563
+#: lib/cli/args.py:559
 msgid ""
 "The output size of extracted faces. Make sure that the model you intend to "
 "train supports your required size. This will only need to be changed for hi-"
@@ -342,7 +334,7 @@ msgstr ""
 "pretende entrenar admite el tamaño deseado. Esto sólo tendrá que ser "
 "cambiado para los modelos de alta resolución."

-#: lib/cli/args.py:575
+#: lib/cli/args.py:571
 msgid ""
 "Extract every 'nth' frame. This option will skip frames when extracting "
 "faces. For example a value of 1 will extract faces from every frame, a value "
@@ -352,7 +344,7 @@ msgstr ""
 "extraer las caras. Por ejemplo, un valor de 1 extraerá las caras de cada "
 "fotograma, un valor de 10 extraerá las caras de cada 10 fotogramas."

-#: lib/cli/args.py:587
+#: lib/cli/args.py:583
 msgid ""
 "Automatically save the alignments file after a set amount of frames. By "
 "default the alignments file is only saved at the end of the extraction "
@@ -368,18 +360,18 @@ msgstr ""
 "ADVERTENCIA: No interrumpa el script al escribir el archivo porque podría "
 "corromperse. Poner a 0 para desactivar"

-#: lib/cli/args.py:599
+#: lib/cli/args.py:595
 msgid "Draw landmarks on the ouput faces for debugging purposes."
 msgstr ""
 "Dibujar puntos de referencia en las caras de salida para fines de depuración."

-#: lib/cli/args.py:605 lib/cli/args.py:614 lib/cli/args.py:622
-#: lib/cli/args.py:629 lib/cli/args.py:847 lib/cli/args.py:858
-#: lib/cli/args.py:866 lib/cli/args.py:885 lib/cli/args.py:891
+#: lib/cli/args.py:601 lib/cli/args.py:610 lib/cli/args.py:618
+#: lib/cli/args.py:625 lib/cli/args.py:843 lib/cli/args.py:854
+#: lib/cli/args.py:862 lib/cli/args.py:881 lib/cli/args.py:887
 msgid "settings"
 msgstr "ajustes"

-#: lib/cli/args.py:606
+#: lib/cli/args.py:602
 msgid ""
 "Don't run extraction in parallel. Will run each part of the extraction "
 "process separately (one after the other) rather than all at the smae time. "
@@ -389,7 +381,7 @@ msgstr ""
 "extracción por separado (una tras otra) en lugar de hacerlo todo al mismo "
 "tiempo. Útil si la VRAM es escasa."

-#: lib/cli/args.py:615
+#: lib/cli/args.py:611
 msgid ""
 "Skips frames that have already been extracted and exist in the alignments "
 "file"
@@ -397,19 +389,19 @@ msgstr ""
 "Omite los fotogramas que ya han sido extraídos y que existen en el archivo "
 "de alineaciones"

-#: lib/cli/args.py:623
+#: lib/cli/args.py:619
 msgid "Skip frames that already have detected faces in the alignments file"
 msgstr ""
 "Omitir los fotogramas que ya tienen caras detectadas en el archivo de "
 "alineaciones"

-#: lib/cli/args.py:630
+#: lib/cli/args.py:626
 msgid "Skip saving the detected faces to disk. Just create an alignments file"
 msgstr ""
 "No guardar las caras detectadas en el disco. Crear sólo un archivo de "
 "alineaciones"

-#: lib/cli/args.py:652
+#: lib/cli/args.py:648
 msgid ""
 "Swap the original faces in a source video/images to your final faces.\n"
 "Conversion plugins can be configured in the 'Settings' Menu"
@@ -419,7 +411,7 @@ msgstr ""
 "Los plugins de conversión pueden ser configurados en el menú "
 "\"Configuración\""

-#: lib/cli/args.py:673
+#: lib/cli/args.py:669
 msgid ""
 "Only required if converting from images to video. Provide The original video "
 "that the source frames were extracted from (for extracting the fps and "
@@ -429,7 +421,7 @@ msgstr ""
 "original del que se extrajeron los fotogramas de origen (para extraer los "
 "fps y el audio)."

-#: lib/cli/args.py:682
+#: lib/cli/args.py:678
 msgid ""
 "Model directory. The directory containing the trained model you wish to use "
 "for conversion."
@@ -437,7 +429,7 @@ msgstr ""
 "Directorio del modelo. El directorio que contiene el modelo entrenado que "
 "desea utilizar para la conversión."

-#: lib/cli/args.py:692
+#: lib/cli/args.py:688
 msgid ""
 "R|Performs color adjustment to the swapped face. Some of these options have "
 "configurable settings in '/config/convert.ini' or 'Settings > Configure "
@@ -477,7 +469,7 @@ msgstr ""
 "colores. Generalmente no da resultados muy satisfactorios.\n"
 "L|none: No realice el ajuste de color."

-#: lib/cli/args.py:719
+#: lib/cli/args.py:715
 msgid ""
 "R|Masker to use. NB: The mask you require must exist within the alignments "
 "file. You can add additional masks with the Mask Tool.\n"
@@ -553,7 +545,7 @@ msgstr ""
 "L|predicted: Si la opción 'Learn Mask' se habilitó durante el entrenamiento, "
 "esto usará la máscara que fue creada por el modelo entrenado."

-#: lib/cli/args.py:757
+#: lib/cli/args.py:753
 msgid ""
 "R|The plugin to use to output the converted images. The writers are "
 "configurable in '/config/convert.ini' or 'Settings > Configure Convert "
@@ -579,11 +571,11 @@ msgstr ""
 "L|pillow: [images] Más lento que opencv, pero tiene más opciones y soporta "
 "más formatos."

-#: lib/cli/args.py:776 lib/cli/args.py:783 lib/cli/args.py:877
+#: lib/cli/args.py:772 lib/cli/args.py:779 lib/cli/args.py:873
 msgid "Frame Processing"
 msgstr "Proceso de fotogramas"

-#: lib/cli/args.py:777
+#: lib/cli/args.py:773
 #, python-format
 msgid ""
 "Scale the final output frames by this amount. 100%% will output the frames "
@@ -593,7 +585,7 @@ msgstr ""
 "a los fotogramas a las dimensiones de origen. 50%% a la mitad de tamaño. "
 "200%% al doble de tamaño"

-#: lib/cli/args.py:784
+#: lib/cli/args.py:780
 msgid ""
 "Frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use "
 "--frame-ranges 10-50 90-100. Frames falling outside of the selected range "
@@ -607,7 +599,7 @@ msgstr ""
 "imágenes, ¡los nombres de los archivos deben terminar con el número de "
 "fotograma!"

-#: lib/cli/args.py:794
+#: lib/cli/args.py:790
 msgid ""
 "If you have not cleansed your alignments file, then you can filter out faces "
 "by defining a folder here that contains the faces extracted from your input "
@@ -623,7 +615,47 @@ msgstr ""
 "especificada. Si se deja en blanco, se convertirán todas las caras que "
 "existan en el archivo de alineaciones."

-#: lib/cli/args.py:848
+#: lib/cli/args.py:804
+msgid ""
+"Optionally filter out people who you do not wish to process by passing in an "
+"image of that person. Should be a front portrait with a single person in the "
+"image. Multiple images can be added space separated. NB: Using face filter "
+"will significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Opcionalmente, puede filtrar las personas que no desea procesar pasando una "
+"imagen de esa persona. Debe ser un retrato frontal con una sola persona en "
+"la imagen. Se pueden añadir varias imágenes separadas por espacios. NB: El "
+"uso del filtro de caras disminuirá significativamente la velocidad de "
+"extracción y no se puede garantizar su precisión."
+
+#: lib/cli/args.py:817
+msgid ""
+"Optionally select people you wish to process by passing in an image of that "
+"person. Should be a front portrait with a single person in the image. "
+"Multiple images can be added space separated. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Opcionalmente, seleccione las personas que desea procesar pasando una imagen "
+"de esa persona. Debe ser un retrato frontal con una sola persona en la "
+"imagen. Se pueden añadir varias imágenes separadas por espacios. NB: El uso "
+"del filtro facial disminuirá significativamente la velocidad de extracción y "
+"no se puede garantizar su precisión."
+
+#: lib/cli/args.py:831
+msgid ""
+"For use with the optional nfilter/filter files. Threshold for positive face "
+"recognition. Lower values are stricter. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Para usar con los archivos opcionales nfilter/filter. Umbral para el "
+"reconocimiento positivo de caras. Los valores más bajos son más estrictos. "
+"NB: El uso del filtro facial disminuirá significativamente la velocidad de "
+"extracción y no se puede garantizar su precisión."
+
+#: lib/cli/args.py:844
 msgid ""
 "The maximum number of parallel processes for performing conversion. "
 "Converting images is system RAM heavy so it is possible to run out of memory "
@@ -640,7 +672,7 @@ msgstr ""
 "procesos que los disponibles en su sistema. Si 'singleprocess' está "
 "habilitado, este ajuste será ignorado."

-#: lib/cli/args.py:859
+#: lib/cli/args.py:855
 msgid ""
 "[LEGACY] This only needs to be selected if a legacy model is being loaded or "
 "if there are multiple models in the model folder"
@@ -648,7 +680,7 @@ msgstr ""
 "[LEGACY] Sólo es necesario seleccionar esta opción si se está cargando un "
 "modelo heredado si hay varios modelos en la carpeta de modelos"

-#: lib/cli/args.py:867
+#: lib/cli/args.py:863
 msgid ""
 "Enable On-The-Fly Conversion. NOT recommended. You should generate a clean "
 "alignments file for your destination video. However, if you wish you can "
@@ -663,7 +695,7 @@ msgstr ""
 "de baja calidad. Si se encuentra un archivo de alineaciones, esta opción "
 "será ignorada."

-#: lib/cli/args.py:878
+#: lib/cli/args.py:874
 msgid ""
 "When used with --frame-ranges outputs the unchanged frames that are not "
 "processed instead of discarding them."
@@ -671,16 +703,16 @@ msgstr ""
 "Cuando se usa con --frame-ranges, la salida incluye los fotogramas no "
 "procesados en vez de descartarlos."

-#: lib/cli/args.py:886
+#: lib/cli/args.py:882
 msgid "Swap the model. Instead converting from of A -> B, converts B -> A"
 msgstr ""
 "Intercambiar el modelo. En vez de convertir de A a B, convierte de B a A"

-#: lib/cli/args.py:892
+#: lib/cli/args.py:888
 msgid "Disable multiprocessing. Slower but less resource intensive."
 msgstr "Desactiva el multiproceso. Es más lento, pero usa menos recursos."

-#: lib/cli/args.py:908
+#: lib/cli/args.py:904
 msgid ""
 "Train a model on extracted original (A) and swap (B) faces.\n"
 "Training models can take a long time. Anything from 24hrs to over a week\n"
@@ -692,11 +724,11 @@ msgstr ""
 "hasta más de una semana.\n"
 "Los plugins de los modelos pueden configurarse en el menú \"Ajustes\""

-#: lib/cli/args.py:927 lib/cli/args.py:936
+#: lib/cli/args.py:923 lib/cli/args.py:932
 msgid "faces"
 msgstr "caras"

-#: lib/cli/args.py:928
+#: lib/cli/args.py:924
 msgid ""
 "Input directory. A directory containing training images for face A. This is "
 "the original face, i.e. the face that you want to remove and replace with "
@@ -706,7 +738,7 @@ msgstr ""
 "para la cara A. Esta es la cara original, es decir, la cara que se quiere "
 "eliminar y sustituir por la cara B."

-#: lib/cli/args.py:937
+#: lib/cli/args.py:933
 msgid ""
 "Input directory. A directory containing training images for face B. This is "
 "the swap face, i.e. the face that you want to place onto the head of person "
@@ -716,12 +748,12 @@ msgstr ""
 "para la cara B. Esta es la cara de intercambio, es decir, la cara que se "
 "quiere colocar en la cabeza de la persona A."

-#: lib/cli/args.py:945 lib/cli/args.py:957 lib/cli/args.py:973
-#: lib/cli/args.py:998 lib/cli/args.py:1008
+#: lib/cli/args.py:941 lib/cli/args.py:953 lib/cli/args.py:969
+#: lib/cli/args.py:994 lib/cli/args.py:1004
 msgid "model"
 msgstr "modelo"

-#: lib/cli/args.py:946
+#: lib/cli/args.py:942
 msgid ""
 "Model directory. This is where the training data will be stored. You should "
 "always specify a new folder for new models. If starting a new model, select "
@@ -735,7 +767,7 @@ msgstr ""
 "carpeta que no exista (que se creará). Si continúa entrenando un modelo "
 "existente, especifique la ubicación del modelo existente."

-#: lib/cli/args.py:958
+#: lib/cli/args.py:954
 msgid ""
 "R|Load the weights from a pre-existing model into a newly created model. For "
 "most models this will load weights from the Encoder of the given model into "
@@ -759,7 +791,7 @@ msgstr ""
 "NB: Los pesos solo se pueden cargar desde modelos del mismo complemento que "
 "desea entrenar."

-#: lib/cli/args.py:974
+#: lib/cli/args.py:970
 msgid ""
 "R|Select which trainer to use. Trainers can be configured from the Settings "
 "menu or the config folder.\n"
@@ -804,7 +836,7 @@ msgstr ""
 "recursos (se necesita una GPU con una buena cantidad de VRAM). Bueno para "
 "los detalles, pero más susceptible a las diferencias de color."

-#: lib/cli/args.py:999
+#: lib/cli/args.py:995
 msgid ""
 "Output a summary of the model and exit. If a model folder is provided then a "
 "summary of the saved model is displayed. Otherwise a summary of the model "
@@ -816,7 +848,7 @@ msgstr ""
 "muestra un resumen del modelo que crearía el complemento elegido y los "
 "ajustes de configuración."

-#: lib/cli/args.py:1009
+#: lib/cli/args.py:1005
 msgid ""
 "Freeze the weights of the model. Freezing weights means that some of the "
 "parameters in the model will no longer continue to learn, but those that are "
@@ -830,12 +862,12 @@ msgstr ""
 "congelará el codificador, pero algunos modelos pueden tener opciones de "
 "configuración para congelar otras capas."

-#: lib/cli/args.py:1022 lib/cli/args.py:1034 lib/cli/args.py:1045
-#: lib/cli/args.py:1056 lib/cli/args.py:1139
+#: lib/cli/args.py:1018 lib/cli/args.py:1030 lib/cli/args.py:1041
+#: lib/cli/args.py:1052 lib/cli/args.py:1135
 msgid "training"
 msgstr "entrenamiento"

-#: lib/cli/args.py:1023
+#: lib/cli/args.py:1019
 msgid ""
 "Batch size. This is the number of images processed through the model for "
 "each side per iteration. NB: As the model is fed 2 sides at a time, the "
@@ -848,7 +880,7 @@ msgstr ""
 "momento es el doble del número que se establece aquí. Los lotes más grandes "
 "requieren más RAM de la GPU."

-#: lib/cli/args.py:1035
+#: lib/cli/args.py:1031
 msgid ""
 "Length of training in iterations. This is only really used for automation. "
 "There is no 'correct' number of iterations a model should be trained for. "
@@ -863,7 +895,7 @@ msgstr ""
 "automáticamente en un número determinado de iteraciones, puede establecer "
 "ese valor aquí."

-#: lib/cli/args.py:1046
+#: lib/cli/args.py:1042
 msgid ""
 "[Deprecated - Use '-D, --distribution-strategy' instead] Use the Tensorflow "
 "Mirrored Distrubution Strategy to train on multiple GPUs."
@@ -871,7 +903,7 @@ msgstr ""
 "[Obsoleto: use '-D, --distribution-strategy' en su lugar] Use la estrategia "
 "de distribución duplicada de Tensorflow para entrenar en varias GPU."

-#: lib/cli/args.py:1057
+#: lib/cli/args.py:1053
 msgid ""
 "R|Select the distribution stategy to use.\n"
 "L|default: Use Tensorflow's default distribution strategy.\n"
@@ -897,15 +929,15 @@ msgstr ""
 "locales. Se carga una copia del modelo y todas las variables en cada GPU con "
 "lotes distribuidos a cada GPU en cada iteración."

-#: lib/cli/args.py:1074 lib/cli/args.py:1084
+#: lib/cli/args.py:1070 lib/cli/args.py:1080
 msgid "Saving"
 msgstr "Guardar"

-#: lib/cli/args.py:1075
+#: lib/cli/args.py:1071
 msgid "Sets the number of iterations between each model save."
 msgstr "Establece el número de iteraciones entre cada guardado del modelo."

-#: lib/cli/args.py:1085
+#: lib/cli/args.py:1081
 msgid ""
 "Sets the number of iterations before saving a backup snapshot of the model "
 "in it's current state. Set to 0 for off."
@@ -913,11 +945,11 @@ msgstr ""
 "Establece el número de iteraciones antes de guardar una copia de seguridad "
 "del modelo en su estado actual. Establece 0 para que esté desactivado."

-#: lib/cli/args.py:1092 lib/cli/args.py:1103 lib/cli/args.py:1114
+#: lib/cli/args.py:1088 lib/cli/args.py:1099 lib/cli/args.py:1110
 msgid "timelapse"
 msgstr "intervalo"

-#: lib/cli/args.py:1093
+#: lib/cli/args.py:1089
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -931,7 +963,7 @@ msgstr ""
 "para crear el timelapse. También debe suministrar un parámetro --timelapse-"
 "output y un parámetro --timelapse-input-B."

-#: lib/cli/args.py:1104
+#: lib/cli/args.py:1100
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -945,7 +977,7 @@ msgstr ""
 "para crear el timelapse. También debe suministrar un parámetro --timelapse-"
 "output y un parámetro --timelapse-input-A."

-#: lib/cli/args.py:1115
+#: lib/cli/args.py:1111
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. If "
@@ -957,17 +989,17 @@ msgstr ""
 "Si se suministran las carpetas de entrada pero no la carpeta de salida, se "
 "guardará por defecto en la carpeta del modelo /timelapse/"

-#: lib/cli/args.py:1124 lib/cli/args.py:1131
+#: lib/cli/args.py:1120 lib/cli/args.py:1127
 msgid "preview"
 msgstr "previsualización"

-#: lib/cli/args.py:1125
+#: lib/cli/args.py:1121
 msgid "Show training preview output. in a separate window."
 msgstr ""
 "Mostrar la salida de la vista previa del entrenamiento. en una ventana "
 "separada."

-#: lib/cli/args.py:1132
+#: lib/cli/args.py:1128
 msgid ""
 "Writes the training result to a file. The image will be stored in the root "
 "of your FaceSwap folder."
@@ -975,7 +1007,7 @@ msgstr ""
 "Escribe el resultado del entrenamiento en un archivo. La imagen se "
 "almacenará en la raíz de su carpeta FaceSwap."

-#: lib/cli/args.py:1140
+#: lib/cli/args.py:1136
 msgid ""
 "Disables TensorBoard logging. NB: Disabling logs means that you will not be "
 "able to use the graph or analysis for this session in the GUI."
@@ -983,12 +1015,12 @@ msgstr ""
 "Desactiva el registro de TensorBoard. NB: Desactivar los registros significa "
 "que no podrá utilizar el gráfico o el análisis de esta sesión en la GUI."

-#: lib/cli/args.py:1147 lib/cli/args.py:1156 lib/cli/args.py:1165
-#: lib/cli/args.py:1174
+#: lib/cli/args.py:1143 lib/cli/args.py:1152 lib/cli/args.py:1161
+#: lib/cli/args.py:1170
 msgid "augmentation"
 msgstr "aumento"

-#: lib/cli/args.py:1148
+#: lib/cli/args.py:1144
 msgid ""
 "Warps training faces to closely matched Landmarks from the opposite face-set "
 "rather than randomly warping the face. This is the 'dfaker' way of doing "
@@ -998,7 +1030,7 @@ msgstr ""
 "conjunto de caras opuestas en lugar de deformar la cara al azar. Esta es la "
 "forma 'dfaker' de hacer la deformación."

-#: lib/cli/args.py:1157
+#: lib/cli/args.py:1153
 msgid ""
 "To effectively learn, a random set of images are flipped horizontally. "
 "Sometimes it is desirable for this not to occur. Generally this should be "
@@ -1009,7 +1041,7 @@ msgstr ""
 "general, esto debería dejarse sin efecto, excepto durante el 'entrenamiento "
 "de ajuste'."

-#: lib/cli/args.py:1166
+#: lib/cli/args.py:1162
 msgid ""
 "Color augmentation helps make the model less susceptible to color "
 "differences between the A and B sets, at an increased training time cost. "
@@ -1019,7 +1051,7 @@ msgstr ""
 "diferencias de color entre los conjuntos A y B, con un mayor coste de tiempo "
 "de entrenamiento. Activa esta opción para desactivar el aumento de color."

-#: lib/cli/args.py:1175
+#: lib/cli/args.py:1171
 msgid ""
 "Warping is integral to training the Neural Network. This option should only "
 "be enabled towards the very end of training to try to bring out more detail. "
@@ -1032,7 +1064,7 @@ msgstr ""
 "esta opción desde el principio, es probable que arruine el modelo y se "
 "obtengan resultados terribles."

-#: lib/cli/args.py:1200
+#: lib/cli/args.py:1196
 msgid "Output to Shell console instead of GUI console"
 msgstr "Salida a la consola Shell en lugar de la consola GUI"

--- a/locales/lib.cli.args.pot
+++ b/locales/lib.cli.args.pot
@@ -8,7 +8,7 @@ msgid ""
 msgstr ""
 "Project-Id-Version: PACKAGE VERSION\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2022-10-10 13:05+0100\n"
+"POT-Creation-Date: 2022-10-31 11:51+0000\n"
 "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 "Language-Team: LANGUAGE <LL@li.org>\n"
@@ -46,7 +46,7 @@ msgid "Path to store the logfile. Leave blank to store in the faceswap folder"
 msgstr ""

 #: lib/cli/args.py:320 lib/cli/args.py:329 lib/cli/args.py:337
-#: lib/cli/args.py:386 lib/cli/args.py:672 lib/cli/args.py:681
+#: lib/cli/args.py:386 lib/cli/args.py:668 lib/cli/args.py:677
 msgid "Data"
 msgstr ""

@@ -82,8 +82,8 @@ msgstr ""

 #: lib/cli/args.py:396 lib/cli/args.py:412 lib/cli/args.py:424
 #: lib/cli/args.py:463 lib/cli/args.py:481 lib/cli/args.py:493
-#: lib/cli/args.py:502 lib/cli/args.py:691 lib/cli/args.py:718
-#: lib/cli/args.py:756
+#: lib/cli/args.py:502 lib/cli/args.py:687 lib/cli/args.py:714
+#: lib/cli/args.py:752
 msgid "Plugins"
 msgstr ""

@@ -180,9 +180,9 @@ msgid ""
 "little, but will save time if using 'sort by face'"
 msgstr ""

-#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:536
-#: lib/cli/args.py:550 lib/cli/args.py:793 lib/cli/args.py:807
-#: lib/cli/args.py:820 lib/cli/args.py:834
+#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:535
+#: lib/cli/args.py:548 lib/cli/args.py:789 lib/cli/args.py:803
+#: lib/cli/args.py:816 lib/cli/args.py:830
 msgid "Face Processing"
 msgstr ""

@@ -192,52 +192,48 @@ msgid ""
 "diagonal of the bounding box. Set to 0 for off"
 msgstr ""

-#: lib/cli/args.py:524 lib/cli/args.py:808
+#: lib/cli/args.py:524
 msgid ""
-"Optionally filter out people who you do not wish to process by passing in an "
-"image of that person. Should be a front portrait with a single person in the "
-"image. Multiple images can be added space separated. NB: Using face filter "
-"will significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally filter out people who you do not wish to extract by passing in "
+"images of those people. Should be a small variety of images at different "
+"angles and in different conditions. Multiple images can be added space "
+"separated."
 msgstr ""

-#: lib/cli/args.py:537 lib/cli/args.py:821
+#: lib/cli/args.py:536
 msgid ""
-"Optionally select people you wish to process by passing in an image of that "
-"person. Should be a front portrait with a single person in the image. "
-"Multiple images can be added space separated. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally select people you wish to extract by passing in images of that "
+"person. Should be a small variety of images at different angles and in "
+"different conditions. Multiple identities can be filtered. Multiple images "
+"can be added space separated."
 msgstr ""

-#: lib/cli/args.py:551 lib/cli/args.py:835
+#: lib/cli/args.py:549
 msgid ""
 "For use with the optional nfilter/filter files. Threshold for positive face "
-"recognition. Lower values are stricter. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"recognition. Higher values are stricter."
 msgstr ""

-#: lib/cli/args.py:562 lib/cli/args.py:574 lib/cli/args.py:586
-#: lib/cli/args.py:598
+#: lib/cli/args.py:558 lib/cli/args.py:570 lib/cli/args.py:582
+#: lib/cli/args.py:594
 msgid "output"
 msgstr ""

-#: lib/cli/args.py:563
+#: lib/cli/args.py:559
 msgid ""
 "The output size of extracted faces. Make sure that the model you intend to "
 "train supports your required size. This will only need to be changed for hi-"
 "res models."
 msgstr ""

-#: lib/cli/args.py:575
+#: lib/cli/args.py:571
 msgid ""
 "Extract every 'nth' frame. This option will skip frames when extracting "
 "faces. For example a value of 1 will extract faces from every frame, a value "
 "of 10 will extract faces from every 10th frame."
 msgstr ""

-#: lib/cli/args.py:587
+#: lib/cli/args.py:583
 msgid ""
 "Automatically save the alignments file after a set amount of frames. By "
 "default the alignments file is only saved at the end of the extraction "
@@ -247,57 +243,57 @@ msgid ""
 "turn off"
 msgstr ""

-#: lib/cli/args.py:599
+#: lib/cli/args.py:595
 msgid "Draw landmarks on the ouput faces for debugging purposes."
 msgstr ""

-#: lib/cli/args.py:605 lib/cli/args.py:614 lib/cli/args.py:622
-#: lib/cli/args.py:629 lib/cli/args.py:847 lib/cli/args.py:858
-#: lib/cli/args.py:866 lib/cli/args.py:885 lib/cli/args.py:891
+#: lib/cli/args.py:601 lib/cli/args.py:610 lib/cli/args.py:618
+#: lib/cli/args.py:625 lib/cli/args.py:843 lib/cli/args.py:854
+#: lib/cli/args.py:862 lib/cli/args.py:881 lib/cli/args.py:887
 msgid "settings"
 msgstr ""

-#: lib/cli/args.py:606
+#: lib/cli/args.py:602
 msgid ""
 "Don't run extraction in parallel. Will run each part of the extraction "
 "process separately (one after the other) rather than all at the smae time. "
 "Useful if VRAM is at a premium."
 msgstr ""

-#: lib/cli/args.py:615
+#: lib/cli/args.py:611
 msgid ""
 "Skips frames that have already been extracted and exist in the alignments "
 "file"
 msgstr ""

-#: lib/cli/args.py:623
+#: lib/cli/args.py:619
 msgid "Skip frames that already have detected faces in the alignments file"
 msgstr ""

-#: lib/cli/args.py:630
+#: lib/cli/args.py:626
 msgid "Skip saving the detected faces to disk. Just create an alignments file"
 msgstr ""

-#: lib/cli/args.py:652
+#: lib/cli/args.py:648
 msgid ""
 "Swap the original faces in a source video/images to your final faces.\n"
 "Conversion plugins can be configured in the 'Settings' Menu"
 msgstr ""

-#: lib/cli/args.py:673
+#: lib/cli/args.py:669
 msgid ""
 "Only required if converting from images to video. Provide The original video "
 "that the source frames were extracted from (for extracting the fps and "
 "audio)."
 msgstr ""

-#: lib/cli/args.py:682
+#: lib/cli/args.py:678
 msgid ""
 "Model directory. The directory containing the trained model you wish to use "
 "for conversion."
 msgstr ""

-#: lib/cli/args.py:692
+#: lib/cli/args.py:688
 msgid ""
 "R|Performs color adjustment to the swapped face. Some of these options have "
 "configurable settings in '/config/convert.ini' or 'Settings > Configure "
@@ -318,7 +314,7 @@ msgid ""
 "L|none: Don't perform color adjustment."
 msgstr ""

-#: lib/cli/args.py:719
+#: lib/cli/args.py:715
 msgid ""
 "R|Masker to use. NB: The mask you require must exist within the alignments "
 "file. You can add additional masks with the Mask Tool.\n"
@@ -355,7 +351,7 @@ msgid ""
 "will use the mask that was created by the trained model."
 msgstr ""

-#: lib/cli/args.py:757
+#: lib/cli/args.py:753
 msgid ""
 "R|The plugin to use to output the converted images. The writers are "
 "configurable in '/config/convert.ini' or 'Settings > Configure Convert "
@@ -370,18 +366,18 @@ msgid ""
 "more formats."
 msgstr ""

-#: lib/cli/args.py:776 lib/cli/args.py:783 lib/cli/args.py:877
+#: lib/cli/args.py:772 lib/cli/args.py:779 lib/cli/args.py:873
 msgid "Frame Processing"
 msgstr ""

-#: lib/cli/args.py:777
+#: lib/cli/args.py:773
 #, python-format
 msgid ""
 "Scale the final output frames by this amount. 100%% will output the frames "
 "at source dimensions. 50%% at half size 200%% at double size"
 msgstr ""

-#: lib/cli/args.py:784
+#: lib/cli/args.py:780
 msgid ""
 "Frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use "
 "--frame-ranges 10-50 90-100. Frames falling outside of the selected range "
@@ -389,7 +385,7 @@ msgid ""
 "converting from images, then the filenames must end with the frame-number!"
 msgstr ""

-#: lib/cli/args.py:794
+#: lib/cli/args.py:790
 msgid ""
 "If you have not cleansed your alignments file, then you can filter out faces "
 "by defining a folder here that contains the faces extracted from your input "
@@ -399,7 +395,33 @@ msgid ""
 "alignments file."
 msgstr ""

-#: lib/cli/args.py:848
+#: lib/cli/args.py:804
+msgid ""
+"Optionally filter out people who you do not wish to process by passing in an "
+"image of that person. Should be a front portrait with a single person in the "
+"image. Multiple images can be added space separated. NB: Using face filter "
+"will significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+
+#: lib/cli/args.py:817
+msgid ""
+"Optionally select people you wish to process by passing in an image of that "
+"person. Should be a front portrait with a single person in the image. "
+"Multiple images can be added space separated. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+
+#: lib/cli/args.py:831
+msgid ""
+"For use with the optional nfilter/filter files. Threshold for positive face "
+"recognition. Lower values are stricter. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+
+#: lib/cli/args.py:844
 msgid ""
 "The maximum number of parallel processes for performing conversion. "
 "Converting images is system RAM heavy so it is possible to run out of memory "
@@ -409,13 +431,13 @@ msgid ""
 "your system. If singleprocess is enabled this setting will be ignored."
 msgstr ""

-#: lib/cli/args.py:859
+#: lib/cli/args.py:855
 msgid ""
 "[LEGACY] This only needs to be selected if a legacy model is being loaded or "
 "if there are multiple models in the model folder"
 msgstr ""

-#: lib/cli/args.py:867
+#: lib/cli/args.py:863
 msgid ""
 "Enable On-The-Fly Conversion. NOT recommended. You should generate a clean "
 "alignments file for your destination video. However, if you wish you can "
@@ -424,51 +446,51 @@ msgid ""
 "alignments file is found, this option will be ignored."
 msgstr ""

-#: lib/cli/args.py:878
+#: lib/cli/args.py:874
 msgid ""
 "When used with --frame-ranges outputs the unchanged frames that are not "
 "processed instead of discarding them."
 msgstr ""

-#: lib/cli/args.py:886
+#: lib/cli/args.py:882
 msgid "Swap the model. Instead converting from of A -> B, converts B -> A"
 msgstr ""

-#: lib/cli/args.py:892
+#: lib/cli/args.py:888
 msgid "Disable multiprocessing. Slower but less resource intensive."
 msgstr ""

-#: lib/cli/args.py:908
+#: lib/cli/args.py:904
 msgid ""
 "Train a model on extracted original (A) and swap (B) faces.\n"
 "Training models can take a long time. Anything from 24hrs to over a week\n"
 "Model plugins can be configured in the 'Settings' Menu"
 msgstr ""

-#: lib/cli/args.py:927 lib/cli/args.py:936
+#: lib/cli/args.py:923 lib/cli/args.py:932
 msgid "faces"
 msgstr ""

-#: lib/cli/args.py:928
+#: lib/cli/args.py:924
 msgid ""
 "Input directory. A directory containing training images for face A. This is "
 "the original face, i.e. the face that you want to remove and replace with "
 "face B."
 msgstr ""

-#: lib/cli/args.py:937
+#: lib/cli/args.py:933
 msgid ""
 "Input directory. A directory containing training images for face B. This is "
 "the swap face, i.e. the face that you want to place onto the head of person "
 "A."
 msgstr ""

-#: lib/cli/args.py:945 lib/cli/args.py:957 lib/cli/args.py:973
-#: lib/cli/args.py:998 lib/cli/args.py:1008
+#: lib/cli/args.py:941 lib/cli/args.py:953 lib/cli/args.py:969
+#: lib/cli/args.py:994 lib/cli/args.py:1004
 msgid "model"
 msgstr ""

-#: lib/cli/args.py:946
+#: lib/cli/args.py:942
 msgid ""
 "Model directory. This is where the training data will be stored. You should "
 "always specify a new folder for new models. If starting a new model, select "
@@ -477,7 +499,7 @@ msgid ""
 "the existing model."
 msgstr ""

-#: lib/cli/args.py:958
+#: lib/cli/args.py:954
 msgid ""
 "R|Load the weights from a pre-existing model into a newly created model. For "
 "most models this will load weights from the Encoder of the given model into "
@@ -491,7 +513,7 @@ msgid ""
 "to train."
 msgstr ""

-#: lib/cli/args.py:974
+#: lib/cli/args.py:970
 msgid ""
 "R|Select which trainer to use. Trainers can be configured from the Settings "
 "menu or the config folder.\n"
@@ -514,7 +536,7 @@ msgid ""
 "susceptible to color differences."
 msgstr ""

-#: lib/cli/args.py:999
+#: lib/cli/args.py:995
 msgid ""
 "Output a summary of the model and exit. If a model folder is provided then a "
 "summary of the saved model is displayed. Otherwise a summary of the model "
@@ -522,7 +544,7 @@ msgid ""
 "displayed."
 msgstr ""

-#: lib/cli/args.py:1009
+#: lib/cli/args.py:1005
 msgid ""
 "Freeze the weights of the model. Freezing weights means that some of the "
 "parameters in the model will no longer continue to learn, but those that are "
@@ -531,12 +553,12 @@ msgid ""
 "layers."
 msgstr ""

-#: lib/cli/args.py:1022 lib/cli/args.py:1034 lib/cli/args.py:1045
-#: lib/cli/args.py:1056 lib/cli/args.py:1139
+#: lib/cli/args.py:1018 lib/cli/args.py:1030 lib/cli/args.py:1041
+#: lib/cli/args.py:1052 lib/cli/args.py:1135
 msgid "training"
 msgstr ""

-#: lib/cli/args.py:1023
+#: lib/cli/args.py:1019
 msgid ""
 "Batch size. This is the number of images processed through the model for "
 "each side per iteration. NB: As the model is fed 2 sides at a time, the "
@@ -544,7 +566,7 @@ msgid ""
 "number that you set here. Larger batches require more GPU RAM."
 msgstr ""

-#: lib/cli/args.py:1035
+#: lib/cli/args.py:1031
 msgid ""
 "Length of training in iterations. This is only really used for automation. "
 "There is no 'correct' number of iterations a model should be trained for. "
@@ -553,13 +575,13 @@ msgid ""
 "can set that value here."
 msgstr ""

-#: lib/cli/args.py:1046
+#: lib/cli/args.py:1042
 msgid ""
 "[Deprecated - Use '-D, --distribution-strategy' instead] Use the Tensorflow "
 "Mirrored Distrubution Strategy to train on multiple GPUs."
 msgstr ""

-#: lib/cli/args.py:1057
+#: lib/cli/args.py:1053
 msgid ""
 "R|Select the distribution stategy to use.\n"
 "L|default: Use Tensorflow's default distribution strategy.\n"
@@ -572,25 +594,25 @@ msgid ""
 "batches distributed to each GPU at each iteration."
 msgstr ""

-#: lib/cli/args.py:1074 lib/cli/args.py:1084
+#: lib/cli/args.py:1070 lib/cli/args.py:1080
 msgid "Saving"
 msgstr ""

-#: lib/cli/args.py:1075
+#: lib/cli/args.py:1071
 msgid "Sets the number of iterations between each model save."
 msgstr ""

-#: lib/cli/args.py:1085
+#: lib/cli/args.py:1081
 msgid ""
 "Sets the number of iterations before saving a backup snapshot of the model "
 "in it's current state. Set to 0 for off."
 msgstr ""

-#: lib/cli/args.py:1092 lib/cli/args.py:1103 lib/cli/args.py:1114
+#: lib/cli/args.py:1088 lib/cli/args.py:1099 lib/cli/args.py:1110
 msgid "timelapse"
 msgstr ""

-#: lib/cli/args.py:1093
+#: lib/cli/args.py:1089
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -599,7 +621,7 @@ msgid ""
 "timelapse-input-B parameter."
 msgstr ""

-#: lib/cli/args.py:1104
+#: lib/cli/args.py:1100
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -608,7 +630,7 @@ msgid ""
 "timelapse-input-A parameter."
 msgstr ""

-#: lib/cli/args.py:1115
+#: lib/cli/args.py:1111
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. If "
@@ -616,53 +638,53 @@ msgid ""
 "model folder /timelapse/"
 msgstr ""

-#: lib/cli/args.py:1124 lib/cli/args.py:1131
+#: lib/cli/args.py:1120 lib/cli/args.py:1127
 msgid "preview"
 msgstr ""

-#: lib/cli/args.py:1125
+#: lib/cli/args.py:1121
 msgid "Show training preview output. in a separate window."
 msgstr ""

-#: lib/cli/args.py:1132
+#: lib/cli/args.py:1128
 msgid ""
 "Writes the training result to a file. The image will be stored in the root "
 "of your FaceSwap folder."
 msgstr ""

-#: lib/cli/args.py:1140
+#: lib/cli/args.py:1136
 msgid ""
 "Disables TensorBoard logging. NB: Disabling logs means that you will not be "
 "able to use the graph or analysis for this session in the GUI."
 msgstr ""

-#: lib/cli/args.py:1147 lib/cli/args.py:1156 lib/cli/args.py:1165
-#: lib/cli/args.py:1174
+#: lib/cli/args.py:1143 lib/cli/args.py:1152 lib/cli/args.py:1161
+#: lib/cli/args.py:1170
 msgid "augmentation"
 msgstr ""

-#: lib/cli/args.py:1148
+#: lib/cli/args.py:1144
 msgid ""
 "Warps training faces to closely matched Landmarks from the opposite face-set "
 "rather than randomly warping the face. This is the 'dfaker' way of doing "
 "warping."
 msgstr ""

-#: lib/cli/args.py:1157
+#: lib/cli/args.py:1153
 msgid ""
 "To effectively learn, a random set of images are flipped horizontally. "
 "Sometimes it is desirable for this not to occur. Generally this should be "
 "left off except for during 'fit training'."
 msgstr ""

-#: lib/cli/args.py:1166
+#: lib/cli/args.py:1162
 msgid ""
 "Color augmentation helps make the model less susceptible to color "
 "differences between the A and B sets, at an increased training time cost. "
 "Enable this option to disable color augmentation."
 msgstr ""

-#: lib/cli/args.py:1175
+#: lib/cli/args.py:1171
 msgid ""
 "Warping is integral to training the Neural Network. This option should only "
 "be enabled towards the very end of training to try to bring out more detail. "
@@ -670,6 +692,6 @@ msgid ""
 "likely to kill a model and lead to terrible results."
 msgstr ""

-#: lib/cli/args.py:1200
+#: lib/cli/args.py:1196
 msgid "Output to Shell console instead of GUI console"
 msgstr ""
--- a/locales/ru/LC_MESSAGES/lib.cli.args.mo
+++ b/locales/ru/LC_MESSAGES/lib.cli.args.mo
--- a/locales/ru/LC_MESSAGES/lib.cli.args.po
+++ b/locales/ru/LC_MESSAGES/lib.cli.args.po
@@ -6,8 +6,8 @@ msgid ""
 msgstr ""
 "Project-Id-Version: \n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2022-10-10 13:05+0100\n"
-"PO-Revision-Date: 2022-10-10 13:08+0100\n"
+"POT-Creation-Date: 2022-10-31 11:51+0000\n"
+"PO-Revision-Date: 2022-10-31 11:54+0000\n"
 "Last-Translator: \n"
 "Language-Team: \n"
 "Language: ru\n"
@@ -58,7 +58,7 @@ msgstr ""
 "с faceswap"

 #: lib/cli/args.py:320 lib/cli/args.py:329 lib/cli/args.py:337
-#: lib/cli/args.py:386 lib/cli/args.py:672 lib/cli/args.py:681
+#: lib/cli/args.py:386 lib/cli/args.py:668 lib/cli/args.py:677
 msgid "Data"
 msgstr "Данные"

@@ -102,8 +102,8 @@ msgstr ""

 #: lib/cli/args.py:396 lib/cli/args.py:412 lib/cli/args.py:424
 #: lib/cli/args.py:463 lib/cli/args.py:481 lib/cli/args.py:493
-#: lib/cli/args.py:502 lib/cli/args.py:691 lib/cli/args.py:718
-#: lib/cli/args.py:756
+#: lib/cli/args.py:502 lib/cli/args.py:687 lib/cli/args.py:714
+#: lib/cli/args.py:752
 msgid "Plugins"
 msgstr "Плагины"

@@ -266,9 +266,9 @@ msgstr ""
 "Получите и сохраните кодировку идентификации лица от VGGFace2. Немного "
 "замедляет извлечение, но сэкономит время при использовании «sort by face»"

-#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:536
-#: lib/cli/args.py:550 lib/cli/args.py:793 lib/cli/args.py:807
-#: lib/cli/args.py:820 lib/cli/args.py:834
+#: lib/cli/args.py:513 lib/cli/args.py:523 lib/cli/args.py:535
+#: lib/cli/args.py:548 lib/cli/args.py:789 lib/cli/args.py:803
+#: lib/cli/args.py:816 lib/cli/args.py:830
 msgid "Face Processing"
 msgstr "Обработка лиц"

@@ -280,51 +280,45 @@ msgstr ""
 "Отбрасывает лица ниже указанного размера. Длина указывается в пикселях по "
 "диагонали. Установите в 0 для отключения"

-#: lib/cli/args.py:524 lib/cli/args.py:808
+#: lib/cli/args.py:524
 msgid ""
-"Optionally filter out people who you do not wish to process by passing in an "
-"image of that person. Should be a front portrait with a single person in the "
-"image. Multiple images can be added space separated. NB: Using face filter "
-"will significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally filter out people who you do not wish to extract by passing in "
+"images of those people. Should be a small variety of images at different "
+"angles and in different conditions. Multiple images can be added space "
+"separated."
 msgstr ""
-"Дополнительно вы можете отфильтровать лица людей, которых вы не хотите "
-"обрабатывать указав изображение этого человека. На изображении должен быть "
-"фронтальный портрет одного человека . Можно указать несколько файлов через "
-"пробел. Прим.: Фильтрация лиц существенно снижает скорость извлечения, при "
-"этом точность не гарантируется."
+"При желании отфильтруйте людей, которых вы не хотите извлекать, передав "
+"изображения этих людей. Должно быть небольшое разнообразие снимков под "
+"разными углами и в разных условиях. Несколько изображений могут быть "
+"добавлены через пробел."

-#: lib/cli/args.py:537 lib/cli/args.py:821
+#: lib/cli/args.py:536
 msgid ""
-"Optionally select people you wish to process by passing in an image of that "
-"person. Should be a front portrait with a single person in the image. "
-"Multiple images can be added space separated. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"Optionally select people you wish to extract by passing in images of that "
+"person. Should be a small variety of images at different angles and in "
+"different conditions. Multiple identities can be filtered. Multiple images "
+"can be added space separated."
 msgstr ""
-"Дополнительно вы можете выбрать людей, которых вы хотели бы включить в "
-"обработку путем указания изображения этого человека. Должен быть фронтальный "
-"портрет с лишь одним человеком на картинке. Можно выбрать несколько "
-"изображений через пробел. Прим.: Использование фильтра существенно замедлит "
-"скорость извлечения. Также точность не гарантируется."
+"При желании выберите людей, которых вы хотите извлечь, передав изображения "
+"этого человека. Должно быть небольшое разнообразие снимков под разными "
+"углами и в разных условиях. Множественные личности могут быть отфильтрованы. "
+"Несколько изображений могут быть добавлены через пробел."

-#: lib/cli/args.py:551 lib/cli/args.py:835
+#: lib/cli/args.py:549
 msgid ""
 "For use with the optional nfilter/filter files. Threshold for positive face "
-"recognition. Lower values are stricter. NB: Using face filter will "
-"significantly decrease extraction speed and its accuracy cannot be "
-"guaranteed."
+"recognition. Higher values are stricter."
 msgstr ""
-"Только при использовании файлов nfilter/filter.  Порог для распознавания "
-"лица. Чем ниже значения, тем строже. Прим.: Использование фильтра лиц "
-"существенно замедлит скорость извлечения. Также точность не гарантируется."
+"Для использования с дополнительными файлами nfilter/filter. Порог "
+"положительного распознавания лиц. Более высокие значения являются более "
+"строгими."

-#: lib/cli/args.py:562 lib/cli/args.py:574 lib/cli/args.py:586
-#: lib/cli/args.py:598
+#: lib/cli/args.py:558 lib/cli/args.py:570 lib/cli/args.py:582
+#: lib/cli/args.py:594
 msgid "output"
 msgstr "вывод"

-#: lib/cli/args.py:563
+#: lib/cli/args.py:559
 msgid ""
 "The output size of extracted faces. Make sure that the model you intend to "
 "train supports your required size. This will only need to be changed for hi-"
@@ -334,7 +328,7 @@ msgstr ""
 "поддерживает такой входной размер. Стоит изменять только для моделей "
 "высокого разрешения."

-#: lib/cli/args.py:575
+#: lib/cli/args.py:571
 msgid ""
 "Extract every 'nth' frame. This option will skip frames when extracting "
 "faces. For example a value of 1 will extract faces from every frame, a value "
@@ -344,7 +338,7 @@ msgstr ""
 "извлечении. Например, значение 1 будет искать лица в каждом кадре, а "
 "значение 10 в каждом 10том кадре."

-#: lib/cli/args.py:587
+#: lib/cli/args.py:583
 msgid ""
 "Automatically save the alignments file after a set amount of frames. By "
 "default the alignments file is only saved at the end of the extraction "
@@ -359,17 +353,17 @@ msgstr ""
 "только во время второго прохода. ВНИМАНИЕ: Не прерывайте выполнение во время "
 "записи, так как это может повлечь порчу файла. Установите в 0 для выключения"

-#: lib/cli/args.py:599
+#: lib/cli/args.py:595
 msgid "Draw landmarks on the ouput faces for debugging purposes."
 msgstr "Рисовать ландмарки на выходных лицах для нужд отладки."

-#: lib/cli/args.py:605 lib/cli/args.py:614 lib/cli/args.py:622
-#: lib/cli/args.py:629 lib/cli/args.py:847 lib/cli/args.py:858
-#: lib/cli/args.py:866 lib/cli/args.py:885 lib/cli/args.py:891
+#: lib/cli/args.py:601 lib/cli/args.py:610 lib/cli/args.py:618
+#: lib/cli/args.py:625 lib/cli/args.py:843 lib/cli/args.py:854
+#: lib/cli/args.py:862 lib/cli/args.py:881 lib/cli/args.py:887
 msgid "settings"
 msgstr "настройки"

-#: lib/cli/args.py:606
+#: lib/cli/args.py:602
 msgid ""
 "Don't run extraction in parallel. Will run each part of the extraction "
 "process separately (one after the other) rather than all at the smae time. "
@@ -379,7 +373,7 @@ msgstr ""
 "стадия извлечения будет запущена отдельно (одна, за другой). Полезно при "
 "нехватке VRAM."

-#: lib/cli/args.py:615
+#: lib/cli/args.py:611
 msgid ""
 "Skips frames that have already been extracted and exist in the alignments "
 "file"
@@ -387,16 +381,16 @@ msgstr ""
 "Пропускать кадры, которые уже были извлечены и существуют в файле "
 "выравнивания"

-#: lib/cli/args.py:623
+#: lib/cli/args.py:619
 msgid "Skip frames that already have detected faces in the alignments file"
 msgstr "Пропускать кадры, для которых в файле выравнивания есть найденные лица"

-#: lib/cli/args.py:630
+#: lib/cli/args.py:626
 msgid "Skip saving the detected faces to disk. Just create an alignments file"
 msgstr ""
 "Не сохранять найденные лица на носитель. Просто создать файл выравнивания"

-#: lib/cli/args.py:652
+#: lib/cli/args.py:648
 msgid ""
 "Swap the original faces in a source video/images to your final faces.\n"
 "Conversion plugins can be configured in the 'Settings' Menu"
@@ -404,7 +398,7 @@ msgstr ""
 "Заменить  оригиналы лица в исходном видео/фотографиях новыми.\n"
 "Плагины конвертации могут быть настроены в меню 'Настройки'"

-#: lib/cli/args.py:673
+#: lib/cli/args.py:669
 msgid ""
 "Only required if converting from images to video. Provide The original video "
 "that the source frames were extracted from (for extracting the fps and "
@@ -414,7 +408,7 @@ msgstr ""
 "Предоставьте исходное видео, из которого были извлечены кадры (для настройки "
 "частоты кадров, а также аудио)."

-#: lib/cli/args.py:682
+#: lib/cli/args.py:678
 msgid ""
 "Model directory. The directory containing the trained model you wish to use "
 "for conversion."
@@ -422,7 +416,7 @@ msgstr ""
 "Папка с моделью. Папка, содержащая обученную модель, которую вы хотите "
 "использовать для преобразования."

-#: lib/cli/args.py:692
+#: lib/cli/args.py:688
 msgid ""
 "R|Performs color adjustment to the swapped face. Some of these options have "
 "configurable settings in '/config/convert.ini' or 'Settings > Configure "
@@ -461,7 +455,7 @@ msgstr ""
 "дает удовлетворительных результатов.\n"
 "L|none: Не производить подгонку цвета."

-#: lib/cli/args.py:719
+#: lib/cli/args.py:715
 msgid ""
 "R|Masker to use. NB: The mask you require must exist within the alignments "
 "file. You can add additional masks with the Mask Tool.\n"
@@ -530,7 +524,7 @@ msgstr ""
 "L| predicted: Если во время обучения была включена опция «Learn Mask», будет "
 "использоваться маска, созданная обученной моделью."

-#: lib/cli/args.py:757
+#: lib/cli/args.py:753
 msgid ""
 "R|The plugin to use to output the converted images. The writers are "
 "configurable in '/config/convert.ini' or 'Settings > Configure Convert "
@@ -556,11 +550,11 @@ msgstr ""
 "L|pillow: [изображения] Более медленный, чем opencv, но имеет больше опций и "
 "поддерживает больше форматов."

-#: lib/cli/args.py:776 lib/cli/args.py:783 lib/cli/args.py:877
+#: lib/cli/args.py:772 lib/cli/args.py:779 lib/cli/args.py:873
 msgid "Frame Processing"
 msgstr "Обработка кадров"

-#: lib/cli/args.py:777
+#: lib/cli/args.py:773
 #, python-format
 msgid ""
 "Scale the final output frames by this amount. 100%% will output the frames "
@@ -570,7 +564,7 @@ msgstr ""
 "кадры в исходном размере. 50%% половина от размера, а 200%% в удвоенном "
 "размере"

-#: lib/cli/args.py:784
+#: lib/cli/args.py:780
 msgid ""
 "Frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use "
 "--frame-ranges 10-50 90-100. Frames falling outside of the selected range "
@@ -583,7 +577,7 @@ msgstr ""
 "unchanged). Прим.: Если при конверсии используются изображения, то имена "
 "файлов должны заканчиваться номером кадра!"

-#: lib/cli/args.py:794
+#: lib/cli/args.py:790
 msgid ""
 "If you have not cleansed your alignments file, then you can filter out faces "
 "by defining a folder here that contains the faces extracted from your input "
@@ -599,7 +593,46 @@ msgstr ""
 "Если оставить это поле пустым, то все лица, которые существуют в файле "
 "выравниваний будут сконвертированы."

-#: lib/cli/args.py:848
+#: lib/cli/args.py:804
+msgid ""
+"Optionally filter out people who you do not wish to process by passing in an "
+"image of that person. Should be a front portrait with a single person in the "
+"image. Multiple images can be added space separated. NB: Using face filter "
+"will significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Дополнительно вы можете отфильтровать лица людей, которых вы не хотите "
+"обрабатывать указав изображение этого человека. На изображении должен быть "
+"фронтальный портрет одного человека . Можно указать несколько файлов через "
+"пробел. Прим.: Фильтрация лиц существенно снижает скорость извлечения, при "
+"этом точность не гарантируется."
+
+#: lib/cli/args.py:817
+msgid ""
+"Optionally select people you wish to process by passing in an image of that "
+"person. Should be a front portrait with a single person in the image. "
+"Multiple images can be added space separated. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Дополнительно вы можете выбрать людей, которых вы хотели бы включить в "
+"обработку путем указания изображения этого человека. Должен быть фронтальный "
+"портрет с лишь одним человеком на картинке. Можно выбрать несколько "
+"изображений через пробел. Прим.: Использование фильтра существенно замедлит "
+"скорость извлечения. Также точность не гарантируется."
+
+#: lib/cli/args.py:831
+msgid ""
+"For use with the optional nfilter/filter files. Threshold for positive face "
+"recognition. Lower values are stricter. NB: Using face filter will "
+"significantly decrease extraction speed and its accuracy cannot be "
+"guaranteed."
+msgstr ""
+"Только при использовании файлов nfilter/filter.  Порог для распознавания "
+"лица. Чем ниже значения, тем строже. Прим.: Использование фильтра лиц "
+"существенно замедлит скорость извлечения. Также точность не гарантируется."
+
+#: lib/cli/args.py:844
 msgid ""
 "The maximum number of parallel processes for performing conversion. "
 "Converting images is system RAM heavy so it is possible to run out of memory "
@@ -616,7 +649,7 @@ msgstr ""
 "будет использоваться больше процессов, чем доступно в вашей системе. Если "
 "включен одиночный процесс, этот параметр будет проигнорирован."

-#: lib/cli/args.py:859
+#: lib/cli/args.py:855
 msgid ""
 "[LEGACY] This only needs to be selected if a legacy model is being loaded or "
 "if there are multiple models in the model folder"
@@ -624,7 +657,7 @@ msgstr ""
 "[СОВМЕСТИМОСТЬ] Это нужно выбирать только в том случае, если загружается "
 "устаревшая модель или если в папке сохранения есть несколько моделей"

-#: lib/cli/args.py:867
+#: lib/cli/args.py:863
 msgid ""
 "Enable On-The-Fly Conversion. NOT recommended. You should generate a clean "
 "alignments file for your destination video. However, if you wish you can "
@@ -638,7 +671,7 @@ msgstr ""
 "использованию улучшенного конвейера экстракции и некачественных результатов. "
 "Если файл выравниваний найден, этот параметр будет проигнорирован."

-#: lib/cli/args.py:878
+#: lib/cli/args.py:874
 msgid ""
 "When used with --frame-ranges outputs the unchanged frames that are not "
 "processed instead of discarding them."
@@ -646,16 +679,16 @@ msgstr ""
 "При использовании с --frame-range кадры не попавшие в диапазон выводятся "
 "неизменными, вместо их пропуска."

-#: lib/cli/args.py:886
+#: lib/cli/args.py:882
 msgid "Swap the model. Instead converting from of A -> B, converts B -> A"
 msgstr ""
 "Поменять модели местами. Вместо преобразования из A -> B, преобразует B -> A"

-#: lib/cli/args.py:892
+#: lib/cli/args.py:888
 msgid "Disable multiprocessing. Slower but less resource intensive."
 msgstr "Отключить многопроцессорность. Медленнее, но менее ресурсоемко."

-#: lib/cli/args.py:908
+#: lib/cli/args.py:904
 msgid ""
 "Train a model on extracted original (A) and swap (B) faces.\n"
 "Training models can take a long time. Anything from 24hrs to over a week\n"
@@ -666,11 +699,11 @@ msgstr ""
 "Обучение моделей может занять долгое время: от 24 часов до недели\n"
 "Каждую модель можно отдельно настроить в меню «Настройки»"

-#: lib/cli/args.py:927 lib/cli/args.py:936
+#: lib/cli/args.py:923 lib/cli/args.py:932
 msgid "faces"
 msgstr "лица"

-#: lib/cli/args.py:928
+#: lib/cli/args.py:924
 msgid ""
 "Input directory. A directory containing training images for face A. This is "
 "the original face, i.e. the face that you want to remove and replace with "
@@ -679,7 +712,7 @@ msgstr ""
 "Входная папка. Папка содержащая изображения для тренировки лица A. Это "
 "исходное лицо т.е. лицо, которое вы хотите убрать, заменив лицом B."

-#: lib/cli/args.py:937
+#: lib/cli/args.py:933
 msgid ""
 "Input directory. A directory containing training images for face B. This is "
 "the swap face, i.e. the face that you want to place onto the head of person "
@@ -688,12 +721,12 @@ msgstr ""
 "Входная папка. Папка содержащая изображения для тренировки лица B. Это новое "
 "лицо т.е. лицо, которое вы хотите поместить на голову человека A."

-#: lib/cli/args.py:945 lib/cli/args.py:957 lib/cli/args.py:973
-#: lib/cli/args.py:998 lib/cli/args.py:1008
+#: lib/cli/args.py:941 lib/cli/args.py:953 lib/cli/args.py:969
+#: lib/cli/args.py:994 lib/cli/args.py:1004
 msgid "model"
 msgstr "модель"

-#: lib/cli/args.py:946
+#: lib/cli/args.py:942
 msgid ""
 "Model directory. This is where the training data will be stored. You should "
 "always specify a new folder for new models. If starting a new model, select "
@@ -707,7 +740,7 @@ msgstr ""
 "будет создана). Если вы хотите продолжить тренировку, выберите папку с уже "
 "существующими сохранениями."

-#: lib/cli/args.py:958
+#: lib/cli/args.py:954
 msgid ""
 "R|Load the weights from a pre-existing model into a newly created model. For "
 "most models this will load weights from the Encoder of the given model into "
@@ -731,7 +764,7 @@ msgstr ""
 "NB: Вес можно загружать только из моделей того же плагина, который вы "
 "собираетесь тренировать."

-#: lib/cli/args.py:974
+#: lib/cli/args.py:970
 msgid ""
 "R|Select which trainer to use. Trainers can be configured from the Settings "
 "menu or the config folder.\n"
@@ -777,7 +810,7 @@ msgstr ""
 "ресурсам (Вам потребуется GPU с хорошим количеством видеопамяти). Хороша для "
 "деталей, но подвержена к неправильной передаче цвета."

-#: lib/cli/args.py:999
+#: lib/cli/args.py:995
 msgid ""
 "Output a summary of the model and exit. If a model folder is provided then a "
 "summary of the saved model is displayed. Otherwise a summary of the model "
@@ -789,7 +822,7 @@ msgstr ""
 "сводная информация о модели, которая будет создана выбранным плагином, и "
 "параметрами конфигурации."

-#: lib/cli/args.py:1009
+#: lib/cli/args.py:1005
 msgid ""
 "Freeze the weights of the model. Freezing weights means that some of the "
 "parameters in the model will no longer continue to learn, but those that are "
@@ -803,12 +836,12 @@ msgstr ""
 "некоторые модели могут иметь параметры конфигурации для замораживания других "
 "слоев."

-#: lib/cli/args.py:1022 lib/cli/args.py:1034 lib/cli/args.py:1045
-#: lib/cli/args.py:1056 lib/cli/args.py:1139
+#: lib/cli/args.py:1018 lib/cli/args.py:1030 lib/cli/args.py:1041
+#: lib/cli/args.py:1052 lib/cli/args.py:1135
 msgid "training"
 msgstr "тренировка"

-#: lib/cli/args.py:1023
+#: lib/cli/args.py:1019
 msgid ""
 "Batch size. This is the number of images processed through the model for "
 "each side per iteration. NB: As the model is fed 2 sides at a time, the "
@@ -821,7 +854,7 @@ msgstr ""
 "изображений в два раза больше этого числа. Увеличение размера партии требует "
 "больше памяти GPU."

-#: lib/cli/args.py:1035
+#: lib/cli/args.py:1031
 msgid ""
 "Length of training in iterations. This is only really used for automation. "
 "There is no 'correct' number of iterations a model should be trained for. "
@@ -835,7 +868,7 @@ msgstr ""
 "Однако, если вы хотите, чтобы тренировка прервалась после указанного кол-ва "
 "итерация, вы можете ввести это здесь."

-#: lib/cli/args.py:1046
+#: lib/cli/args.py:1042
 msgid ""
 "[Deprecated - Use '-D, --distribution-strategy' instead] Use the Tensorflow "
 "Mirrored Distrubution Strategy to train on multiple GPUs."
@@ -844,7 +877,7 @@ msgstr ""
 "Используйте стратегию зеркального распространения Tensorflow для обучения на "
 "нескольких графических процессорах."

-#: lib/cli/args.py:1057
+#: lib/cli/args.py:1053
 msgid ""
 "R|Select the distribution stategy to use.\n"
 "L|default: Use Tensorflow's default distribution strategy.\n"
@@ -869,15 +902,15 @@ msgstr ""
 "в каждый GPU, причем пакеты распределяются между каждым GPU на каждой "
 "итерации."

-#: lib/cli/args.py:1074 lib/cli/args.py:1084
+#: lib/cli/args.py:1070 lib/cli/args.py:1080
 msgid "Saving"
 msgstr "Сохранение"

-#: lib/cli/args.py:1075
+#: lib/cli/args.py:1071
 msgid "Sets the number of iterations between each model save."
 msgstr "Установка количества итераций между сохранениями модели."

-#: lib/cli/args.py:1085
+#: lib/cli/args.py:1081
 msgid ""
 "Sets the number of iterations before saving a backup snapshot of the model "
 "in it's current state. Set to 0 for off."
@@ -885,11 +918,11 @@ msgstr ""
 "Устанавливает кол-во итераций перед созданием резервной копии модели. "
 "Установите в 0 для отключения."

-#: lib/cli/args.py:1092 lib/cli/args.py:1103 lib/cli/args.py:1114
+#: lib/cli/args.py:1088 lib/cli/args.py:1099 lib/cli/args.py:1110
 msgid "timelapse"
 msgstr "таймлапс"

-#: lib/cli/args.py:1093
+#: lib/cli/args.py:1089
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -902,7 +935,7 @@ msgstr ""
 "папку лиц набора 'A' для использования при создании таймлапса. Вам также "
 "нужно указать параметры--timelapse-output и --timelapse-input-B."

-#: lib/cli/args.py:1104
+#: lib/cli/args.py:1100
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. "
@@ -916,7 +949,7 @@ msgstr ""
 "таймлапса. Вы также должны указать параметр --timelapse-output и --timelapse-"
 "input-A."

-#: lib/cli/args.py:1115
+#: lib/cli/args.py:1111
 msgid ""
 "Optional for creating a timelapse. Timelapse will save an image of your "
 "selected faces into the timelapse-output folder at every save iteration. If "
@@ -928,15 +961,15 @@ msgstr ""
 "указаны только входные папки, то по умолчанию вывод будет сохранен вместе с "
 "моделью в подкаталог /timelapse/"

-#: lib/cli/args.py:1124 lib/cli/args.py:1131
+#: lib/cli/args.py:1120 lib/cli/args.py:1127
 msgid "preview"
 msgstr "предварительный просмотр"

-#: lib/cli/args.py:1125
+#: lib/cli/args.py:1121
 msgid "Show training preview output. in a separate window."
 msgstr "Показывать предварительный просмотр в отдельном окне."

-#: lib/cli/args.py:1132
+#: lib/cli/args.py:1128
 msgid ""
 "Writes the training result to a file. The image will be stored in the root "
 "of your FaceSwap folder."
@@ -944,7 +977,7 @@ msgstr ""
 "Записывает результат тренировки в файл. Файл будет сохранен в коренной папке "
 "FaceSwap."

-#: lib/cli/args.py:1140
+#: lib/cli/args.py:1136
 msgid ""
 "Disables TensorBoard logging. NB: Disabling logs means that you will not be "
 "able to use the graph or analysis for this session in the GUI."
@@ -952,12 +985,12 @@ msgstr ""
 "Отключает журнал TensorBoard. Примечание: Отключение журналов означает, что "
 "вы не сможете использовать графики или анализ сессии внутри GUI."

-#: lib/cli/args.py:1147 lib/cli/args.py:1156 lib/cli/args.py:1165
-#: lib/cli/args.py:1174
+#: lib/cli/args.py:1143 lib/cli/args.py:1152 lib/cli/args.py:1161
+#: lib/cli/args.py:1170
 msgid "augmentation"
 msgstr "аугментация"

-#: lib/cli/args.py:1148
+#: lib/cli/args.py:1144
 msgid ""
 "Warps training faces to closely matched Landmarks from the opposite face-set "
 "rather than randomly warping the face. This is the 'dfaker' way of doing "
@@ -967,7 +1000,7 @@ msgstr ""
 "Ориентирами/Landmarks противоположного набора лиц. Этот способ используется "
 "пакетом \"dfaker\"."

-#: lib/cli/args.py:1157
+#: lib/cli/args.py:1153
 msgid ""
 "To effectively learn, a random set of images are flipped horizontally. "
 "Sometimes it is desirable for this not to occur. Generally this should be "
@@ -978,7 +1011,7 @@ msgstr ""
 "происходило. Как правило, эту настройку не стоит трогать, за исключением "
 "периода «финальной шлифовки»."

-#: lib/cli/args.py:1166
+#: lib/cli/args.py:1162
 msgid ""
 "Color augmentation helps make the model less susceptible to color "
 "differences between the A and B sets, at an increased training time cost. "
@@ -988,7 +1021,7 @@ msgstr ""
 "цвета между наборами A and B ценой некоторого замедления скорости "
 "тренировки. Включите эту опцию для отключения цветовой аугментации."

-#: lib/cli/args.py:1175
+#: lib/cli/args.py:1171
 msgid ""
 "Warping is integral to training the Neural Network. This option should only "
 "be enabled towards the very end of training to try to bring out more detail. "
@@ -1001,7 +1034,7 @@ msgstr ""
 "Включение этой опции с самого начала может убить модель и привести к ужасным "
 "результатам."

-#: lib/cli/args.py:1200
+#: lib/cli/args.py:1196
 msgid "Output to Shell console instead of GUI console"
 msgstr "Вывод в системную консоль вместо GUI"

--- a/plugins/extract/align/_base.py
+++ b/plugins/extract/align/_base.py
@@ -175,8 +175,8 @@ class Aligner(Extractor):  # pylint:disable=abstract-method
                logger.trace("EOF received")  # type:ignore
                exhausted = True
                break
-            # Put frames with no faces into the out queue to keep TQDM consistent
-            if not item.detected_faces:
+            # Put frames with no faces or are already aligned into the out queue
+            if not item.detected_faces or item.is_aligned:
                self._queues["out"].put(item)
                continue

@@ -192,7 +192,8 @@ class Aligner(Extractor):  # pylint:disable=abstract-method
                        self._rollover = ExtractMedia(
                            item.filename,
                            item.image,
-                            detected_faces=item.detected_faces[f_idx + 1:])
+                            detected_faces=item.detected_faces[f_idx + 1:],
+                            is_aligned=item.is_aligned)
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                                     item.filename)
@@ -536,7 +537,7 @@ class AlignedFilter():

        Parameters
        ----------
-        batch: list
+        faces: list
            List of detected face objects to filter out on size
        minimum_dimension: int
            The minimum (height, width) of the original frame
@@ -655,4 +656,4 @@ class AlignedFilter():
                  for key, count in self._counts.items()
                  if count > 0]
        if counts:
-            logger.info("Aligner filtered: [%s)", ", ".join(counts))
+            logger.info("Aligner filtered: (%s)", ", ".join(counts))
--- a/plugins/extract/detect/_base.py
+++ b/plugins/extract/detect/_base.py
@@ -159,6 +159,10 @@ class Detector(Extractor):  # pylint:disable=abstract-method
                exhausted = True
                break
            assert isinstance(item, ExtractMedia)
+            # Put items that are already aligned into the out queue
+            if item.is_aligned:
+                self._queues["out"].put(item)
+                continue
            batch.filename.append(item.filename)
            image, scale, pad = self._compile_detection_image(item)
            batch.image.append(image)
--- a/plugins/extract/mask/_base.py
+++ b/plugins/extract/mask/_base.py
@@ -61,9 +61,6 @@ class Masker(Extractor):  # pylint:disable=abstract-method
        https://github.com/deepfakes-models/faceswap-models for more information
    model_filename: str
        The name of the model file to be loaded
-    image_is_aligned: bool, optional
-        Indicates that the passed in image is an aligned face rather than a frame.
-        Default: ``False``

    Other Parameters
    ----------------
@@ -84,10 +81,8 @@ class Masker(Extractor):  # pylint:disable=abstract-method
                 model_filename: Optional[str] = None,
                 configfile: Optional[str] = None,
                 instance: int = 0,
-                 image_is_aligned=False,
                 **kwargs) -> None:
-        logger.debug("Initializing %s: (configfile: %s, image_is_aligned: %s)",
-                     self.__class__.__name__, configfile, image_is_aligned)
+        logger.debug("Initializing %s: (configfile: %s)", self.__class__.__name__, configfile)
        super().__init__(git_model_id,
                         model_filename,
                         configfile=configfile,
@@ -97,7 +92,6 @@ class Masker(Extractor):  # pylint:disable=abstract-method
        self.coverage_ratio = 1.0  # Override for model specific coverage_ratio

        self._plugin_type = "mask"
-        self._image_is_aligned = image_is_aligned
        self._storage_name = self.__module__.rsplit(".", maxsplit=1)[-1].replace("_", "-")
        self._storage_centering: "CenteringType" = "face"  # Centering to store the mask at
        self._storage_size = 128  # Size to store masks at. Leave this at default
@@ -154,7 +148,7 @@ class Masker(Extractor):  # pylint:disable=abstract-method
                image = item.get_image_copy(self.color_format)
                roi = np.ones((*item.image_size[:2], 1), dtype="float32")

-                if not self._image_is_aligned:
+                if not item.is_aligned:
                    # Add the ROI mask to image so we can get the ROI mask with a single warp
                    image = np.concatenate([image, roi], axis=-1)

@@ -164,10 +158,10 @@ class Masker(Extractor):  # pylint:disable=abstract-method
                                        size=self.input_size,
                                        coverage_ratio=self.coverage_ratio,
                                        dtype="float32",
-                                        is_aligned=self._image_is_aligned)
+                                        is_aligned=item.is_aligned)

                assert feed_face.face is not None
-                if not self._image_is_aligned:
+                if not item.is_aligned:
                    # Split roi mask from feed face alpha channel
                    roi_mask = feed_face.face[..., 3]
                    feed_face._face = feed_face.face[..., :3]  # pylint:disable=protected-access
@@ -189,7 +183,8 @@ class Masker(Extractor):  # pylint:disable=abstract-method
                        self._rollover = ExtractMedia(
                            item.filename,
                            item.image,
-                            detected_faces=item.detected_faces[f_idx + 1:])
+                            detected_faces=item.detected_faces[f_idx + 1:],
+                            is_aligned=item.is_aligned)
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                                     item.filename)
--- a/plugins/extract/pipeline.py
+++ b/plugins/extract/pipeline.py
@@ -12,7 +12,7 @@ plugins either in parallel or in series, giving easy access to input and output.

 import logging
 import sys
-from typing import Any, cast, Dict, Generator, List, Optional, Tuple, TYPE_CHECKING, Union
+from typing import cast, Dict, Generator, List, Optional, Tuple, TYPE_CHECKING, Union

 import cv2

@@ -28,6 +28,7 @@ else:

 if TYPE_CHECKING:
    import numpy as np
+    from lib.align.alignments import PNGHeaderSourceDict
    from lib.align.detected_face import DetectedFace
    from plugins.extract._base import Extractor as PluginExtractor
    from plugins.extract.detect._base import Detector
@@ -91,9 +92,6 @@ class Extractor():
        Default: `0`
    disable_filter: bool, optional
        Disable all aligner filters regardless of config option. Default: ``False``
-    image_is_aligned: bool, optional
-        Used to set the :attr:`plugins.extract.mask.image_is_aligned` attribute. Indicates to the
-        masker that the fed in image is an aligned face rather than a frame. Default: ``False``

    Attributes
    ----------
@@ -113,14 +111,13 @@ class Extractor():
                 min_size: int = 0,
                 normalize_method:  Optional[Literal["none", "clahe", "hist", "mean"]] = None,
                 re_feed: int = 0,
-                 disable_filter: bool = False,
-                 image_is_aligned: bool = False,) -> None:
+                 disable_filter: bool = False) -> None:
        logger.debug("Initializing %s: (detector: %s, aligner: %s, masker: %s, recognition: %s, "
                     "configfile: %s, multiprocess: %s, exclude_gpus: %s, rotate_images: %s, "
-                     "min_size: %s, normalize_method: %s, re_feed: %s, disable_filter: %s, "
-                     "image_is_aligned: %s)", self.__class__.__name__, detector, aligner, masker,
-                     recognition, configfile, multiprocess, exclude_gpus, rotate_images, min_size,
-                     normalize_method, re_feed, disable_filter, image_is_aligned)
+                     "min_size: %s, normalize_method: %s, re_feed: %s, disable_filter: %s, )",
+                     self.__class__.__name__, detector, aligner, masker, recognition, configfile,
+                     multiprocess, exclude_gpus, rotate_images, min_size, normalize_method,
+                     re_feed, disable_filter)
        self._instance = _get_instance()
        maskers = [cast(Optional[str],
                   masker)] if not isinstance(masker, list) else cast(List[Optional[str]], masker)
@@ -139,7 +136,7 @@ class Extractor():
                                       re_feed,
                                       disable_filter)
        self._recognition = self._load_recognition(recognition, configfile)
-        self._mask = [self._load_mask(mask, image_is_aligned, configfile) for mask in maskers]
+        self._mask = [self._load_mask(mask, configfile) for mask in maskers]
        self._is_parallel = self._set_parallel_processing(multiprocess)
        self._phases = self._set_phases(multiprocess)
        self._phase_index = 0
@@ -218,6 +215,18 @@ class Extractor():
        logger.trace(retval)  # type: ignore
        return retval

+    @property
+    def aligner(self) -> "Aligner":
+        """ The currently selected aligner plugin """
+        assert self._align is not None
+        return self._align
+
+    @property
+    def recognition(self) -> "Identity":
+        """ The currently selected recognition plugin """
+        assert self._recognition is not None
+        return self._recognition
+
    def reset_phase_index(self) -> None:
        """ Reset the current phase index back to 0. Used for when batch processing is used in
        extract. """
@@ -625,16 +634,27 @@ class Extractor():

    def _load_mask(self,
                   masker: Optional[str],
-                   image_is_aligned: bool,
                   configfile: Optional[str]) -> Optional["Masker"]:
-        """ Set global arguments and load masker plugin """
+        """ Set global arguments and load masker plugin
+
+        Parameters
+        ----------
+        masker: str or ``none``
+            The name of the masker plugin to use or ``None`` if no masker
+        configfile: str
+            Full path to custom config.ini file or ``None`` to use default
+
+        Returns
+        -------
+        :class:`~plugins.extract.mask._base.Masker` or ``None``
+            The masker plugin to use or ``None`` if no masker selected
+        """
        if masker is None or masker.lower() == "none":
            logger.debug("No masker selected. Returning None")
            return None
        masker_name = masker.replace("-", "_").lower()
        logger.debug("Loading Masker: '%s'", masker_name)
        plugin = PluginLoader.get_masker(masker_name)(exclude_gpus=self._exclude_gpus,
-                                                      image_is_aligned=image_is_aligned,
                                                      configfile=configfile,
                                                      instance=self._instance)
        return plugin
@@ -700,21 +720,6 @@ class Extractor():
                          - plugins_required) // len(gpu_plugins)
        self._set_plugin_batchsize(gpu_plugins, available_vram)

-    def set_aligner_normalization_method(self, method: Optional[Literal["none",
-                                                                        "clahe",
-                                                                        "hist",
-                                                                        "mean"]]) -> None:
-        """ Change the normalization method for faces fed into the aligner.
-
-        Parameters
-        ----------
-        method: {"none", "clahe", "hist", "mean"}
-            The normalization method to apply to faces prior to feeding into the aligner's model
-        """
-        assert self._align is not None
-        logger.debug("Setting to: '%s'", method)
-        self._align.set_normalize_method(method)
-
    def _set_plugin_batchsize(self, gpu_plugins: List[str], available_vram: float) -> None:
        """ Set the batch size for the given plugin based on given available vram.
        Do not update plugins which have a vram_per_batch of 0 (CPU plugins) due to
@@ -779,26 +784,32 @@ class ExtractMedia():
    filename: str
        The base name of the original frame's filename
    image: :class:`numpy.ndarray`
-        The original frame
+        The original frame or a faceswap aligned face image
    detected_faces: list, optional
        A list of :class:`~lib.align.DetectedFace` objects. Detected faces can be added
        later with :func:`add_detected_faces`. Setting ``None`` will default to an empty list.
        Default: ``None``
+    is_aligned: bool, optional
+        ``True`` if the :attr:`image` is an aligned faceswap image otherwise ``False``. Used for
+        face filtering with vggface2. Aligned faceswap images will automatically skip detection,
+        alignment and masking. Default: ``False``
    """

    def __init__(self,
                 filename: str,
                 image: "np.ndarray",
-                 detected_faces: Optional[List["DetectedFace"]] = None) -> None:
+                 detected_faces: Optional[List["DetectedFace"]] = None,
+                 is_aligned: bool = False) -> None:
        logger.trace("Initializing %s: (filename: '%s', image shape: %s, "  # type: ignore
-                     "detected_faces: %s)", self.__class__.__name__, filename, image.shape,
-                     detected_faces)
+                     "detected_faces: %s, is_aligned: %s)", self.__class__.__name__, filename,
+                     image.shape, detected_faces, is_aligned)
        self._filename = filename
        self._image: Optional["np.ndarray"] = image
        self._image_shape = cast(Tuple[int, int, int], image.shape)
        self._detected_faces: List["DetectedFace"] = ([] if detected_faces is None
                                                      else detected_faces)
-        self._frame_metadata: Dict[str, Any] = {}
+        self._is_aligned = is_aligned
+        self._frame_metadata: Optional["PNGHeaderSourceDict"] = None
        self._sub_folders: List[Optional[str]] = []

    @property
@@ -828,7 +839,12 @@ class ExtractMedia():
        return self._detected_faces

    @property
-    def frame_metadata(self) -> dict:
+    def is_aligned(self) -> bool:
+        """ bool. ``True`` if :attr:`image` is an aligned faceswap image otherwise ``False`` """
+        return self._is_aligned
+
+    @property
+    def frame_metadata(self) -> "PNGHeaderSourceDict":
        """ dict: The frame metadata that has been added from an aligned image. This property
        should only be called after :func:`add_frame_metadata` has been called when processing
        an aligned face. For all other instances an assertion error will be raised.
@@ -915,7 +931,7 @@ class ExtractMedia():
                     self._filename, image.shape)
        self._image = image

-    def add_frame_metadata(self, metadata: Dict[str, Any]) -> None:
+    def add_frame_metadata(self, metadata: "PNGHeaderSourceDict") -> None:
        """ Add the source frame metadata from an aligned PNG's header data.

        metadata: dict
@@ -923,7 +939,7 @@ class ExtractMedia():
        """
        logger.trace("Adding PNG Source data for '%s': %s",  # type:ignore
                     self._filename, metadata)
-        dims: Tuple[int, int] = metadata["source_frame_dims"]
+        dims = cast(Tuple[int, int], metadata["source_frame_dims"])
        self._image_shape = (*dims, 3)
        self._frame_metadata = metadata

--- a/plugins/extract/recognition/_base.py
+++ b/plugins/extract/recognition/_base.py
@@ -16,6 +16,7 @@ To get a :class:`~lib.align.DetectedFace` object use the function:
 >>> face = self.to_detected_face(<face left>, <face top>, <face right>, <face bottom>)
 """
 import logging
+import sys

 from dataclasses import dataclass, field
 from typing import Generator, List, Optional, Tuple, TYPE_CHECKING
@@ -23,14 +24,20 @@ from typing import Generator, List, Optional, Tuple, TYPE_CHECKING
 import numpy as np
 from tensorflow.python.framework import errors_impl as tf_errors  # pylint:disable=no-name-in-module  # noqa

-from lib.align import AlignedFace
+from lib.align import AlignedFace, DetectedFace
+from lib.image import read_image_meta
 from lib.utils import FaceswapError, get_backend
 from plugins.extract._base import BatchType, Extractor, ExtractorBatch
 from plugins.extract.pipeline import ExtractMedia

+if sys.version_info < (3, 8):
+    from typing_extensions import get_args, Literal
+else:
+    from typing import get_args, Literal
+
+
 if TYPE_CHECKING:
    from queue import Queue
-    from lib.align import DetectedFace
    from lib.align.aligned_face import CenteringType

 logger = logging.getLogger(__name__)
@@ -58,9 +65,6 @@ class Identity(Extractor):  # pylint:disable=abstract-method
        https://github.com/deepfakes-models/faceswap-models for more information
    model_filename: str
        The name of the model file to be loaded
-    image_is_aligned: bool, optional
-        Indicates that the passed in image is an aligned face rather than a frame.
-        Default: ``False``

    Other Parameters
    ----------------
@@ -81,7 +85,6 @@ class Identity(Extractor):  # pylint:disable=abstract-method
                 model_filename: Optional[str] = None,
                 configfile: Optional[str] = None,
                 instance: int = 0,
-                 image_is_aligned=False,
                 **kwargs):
        logger.debug("Initializing %s", self.__class__.__name__)
        super().__init__(git_model_id,
@@ -94,9 +97,27 @@ class Identity(Extractor):  # pylint:disable=abstract-method
        self.coverage_ratio = 1.0  # Override for model specific coverage_ratio

        self._plugin_type = "recognition"
-        self._image_is_aligned = image_is_aligned
+        self._filter = IdentityFilter(self.config["save_filtered"])
        logger.debug("Initialized _base %s", self.__class__.__name__)

+    def _get_detected_from_aligned(self, item: ExtractMedia) -> None:
+        """ Obtain detected face objects for when loading in aligned faces and a detected face
+        object does not exist
+
+        Parameters
+        ----------
+        item: :class:`~plugins.extract.pipeline.ExtractMedia`
+            The extract media to populate the detected face for
+         """
+        detected_face = DetectedFace()
+        meta = read_image_meta(item.filename).get("itxt", {}).get("alignments")
+        if meta:
+            detected_face.from_png_meta(meta)
+        item.add_detected_faces([detected_face])
+        self._faces_per_filename[item.filename] += 1  # Track this added face
+        logger.debug("Obtained detected face: (filename: %s, detected_face: %s)",
+                     item.filename, item.detected_faces)
+
    def get_batch(self, queue: "Queue") -> Tuple[bool, RecogBatch]:
        """ Get items for inputting into the recognition from the queue in batches

@@ -140,9 +161,12 @@ class Identity(Extractor):  # pylint:disable=abstract-method
                exhausted = True
                break
            # Put frames with no faces into the out queue to keep TQDM consistent
-            if not item.detected_faces:
+            if not item.is_aligned and not item.detected_faces:
                self._queues["out"].put(item)
                continue
+            if item.is_aligned and not item.detected_faces:
+                self._get_detected_from_aligned(item)
+
            for f_idx, face in enumerate(item.detected_faces):

                image = item.get_image_copy(self.color_format)
@@ -152,7 +176,7 @@ class Identity(Extractor):  # pylint:disable=abstract-method
                                        size=self.input_size,
                                        coverage_ratio=self.coverage_ratio,
                                        dtype="float32",
-                                        is_aligned=self._image_is_aligned)
+                                        is_aligned=item.is_aligned)

                batch.detected_faces.append(face)
                batch.feed_faces.append(feed_face)
@@ -164,7 +188,8 @@ class Identity(Extractor):  # pylint:disable=abstract-method
                        self._rollover = ExtractMedia(
                            item.filename,
                            item.image,
-                            detected_faces=item.detected_faces[f_idx + 1:])
+                            detected_faces=item.detected_faces[f_idx + 1:],
+                            is_aligned=item.is_aligned)
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                                     item.filename)
@@ -175,6 +200,11 @@ class Identity(Extractor):  # pylint:disable=abstract-method
                          for k, v in batch.__dict__.items()})
        else:
            logger.trace(item)  # type:ignore
+
+        # TODO Move to end of process not beginning
+        if exhausted:
+            self._filter.output_counts()
+
        return exhausted, batch

    def _predict(self, batch: BatchType) -> RecogBatch:
@@ -239,15 +269,226 @@ class Identity(Extractor):  # pylint:disable=abstract-method
        logger.trace("Item out: %s",  # type: ignore
                     {key: val.shape if isinstance(val, np.ndarray) else val
                                      for key, val in batch.__dict__.items()})
+
        for filename, face in zip(batch.filename, batch.detected_faces):
            self._output_faces.append(face)
            if len(self._output_faces) != self._faces_per_filename[filename]:
                continue

            output = self._extract_media.pop(filename)
+            self._output_faces = self._filter(self._output_faces, output.sub_folders)
+
            output.add_detected_faces(self._output_faces)
            self._output_faces = []
            logger.trace("Yielding: (filename: '%s', image: %s, "  # type:ignore
                         "detected_faces: %s)", output.filename, output.image_shape,
                         len(output.detected_faces))
            yield output
+
+    def add_identity_filters(self,
+                             filters: np.ndarray,
+                             nfilters: np.ndarray,
+                             threshold: float) -> None:
+        """ Add identity encodings to filter by identity in the recognition plugin
+
+        Parameters
+        ----------
+        filters: :class:`numpy.ndarray`
+            The array of filter embeddings to use
+        nfilters: :class:`numpy.ndarray`
+            The array of nfilter embeddings to use
+        threshold: float
+            The threshold for a positive filter match
+        """
+        logger.debug("Adding identity filters")
+        self._filter.add_filters(filters, nfilters, threshold)
+        logger.debug("Added identity filters")
+
+
+class IdentityFilter():
+    """ Applies filters on the output of the recognition plugin
+
+    Parameters
+    ----------
+    save_output: bool
+        ``True`` if the filtered faces should be kept as they are being saved. ``False`` if they
+        should be deleted
+    """
+    def __init__(self, save_output: bool) -> None:
+        logger.debug("Initializing %s: (save_output: %s)", self.__class__.__name__, save_output)
+        self._save_output = save_output
+        self._filter: Optional[np.ndarray] = None
+        self._nfilter: Optional[np.ndarray] = None
+        self._threshold = 0.0
+        self._filter_enabled: bool = False
+        self._nfilter_enabled: bool = False
+        self._active: bool = False
+        self._counts = 0
+        logger.debug("Initialized %s", self.__class__.__name__)
+
+    def add_filters(self, filters: np.ndarray, nfilters: np.ndarray, threshold) -> None:
+        """ Add identity encodings to the filter and set whether each filter is enabled
+
+        Parameters
+        ----------
+        filters: :class:`numpy.ndarray`
+            The array of filter embeddings to use
+        nfilters: :class:`numpy.ndarray`
+            The array of nfilter embeddings to use
+        threshold: float
+            The threshold for a positive filter match
+        """
+        logger.debug("Adding filters: %s, nfilters: %s, threshold: %s",
+                     filters.shape, nfilters.shape, threshold)
+        self._filter = filters
+        self._nfilter = nfilters
+        self._threshold = threshold
+        self._filter_enabled = bool(np.any(self._filter))
+        self._nfilter_enabled = bool(np.any(self._nfilter))
+        self._active = self._filter_enabled or self._nfilter_enabled
+        logger.debug("filter active: %s, nfilter active: %s, all active: %s",
+                     self._filter_enabled, self._nfilter_enabled, self._active)
+
+    @classmethod
+    def _find_cosine_similiarity(cls,
+                                 source_identities: np.ndarray,
+                                 test_identity: np.ndarray) -> np.ndarray:
+        """ Find the cosine similarity between a source face identity and a test face identity
+
+        Parameters
+        ---------
+        source_identities: :class:`numpy.ndarray`
+            The identity encoding for the source face identities
+        test_identity: :class:`numpy.ndarray`
+            The identity encoding for the face identity to test against the sources
+
+        Returns
+        -------
+        :class:`numpy.ndarray`:
+            The cosine similarity between a face identity and the source identities
+        """
+        s_norm = np.linalg.norm(source_identities, axis=1)
+        i_norm = np.linalg.norm(test_identity)
+        retval = source_identities @ test_identity / (s_norm * i_norm)
+        return retval
+
+    def _get_matches(self,
+                     filter_type: Literal["filter", "nfilter"],
+                     identities: np.ndarray) -> np.ndarray:
+        """ Obtain the average and minimum distances for each face against the source identities
+        to test against
+
+        Parameters
+        ----------
+        filter_type ["filter", "nfilter"]
+            The filter type to use for calculating the distance
+        identities: :class:`numpy.ndarray`
+            The identity encodings for the current face(s) being checked
+
+        Returns
+        -------
+        :class:`numpy.ndarray`
+            Boolean array. ``True`` if identity should be filtered otherwise ``False``
+        """
+        encodings = self._filter if filter_type == "filter" else self._nfilter
+        assert encodings is not None
+        distances = np.array([self._find_cosine_similiarity(encodings, identity)
+                              for identity in identities])
+        is_match = np.any(distances >= self._threshold, axis=-1)
+        # Invert for filter (set the `True` match to `False` for should filter)
+        retval = np.invert(is_match) if filter_type == "filter" else is_match
+        logger.trace("filter_type: %s, distances shape: %s, is_match: %s, ",  # type: ignore
+                     "retval: %s", filter_type, distances.shape, is_match, retval)
+        return retval
+
+    def _filter_faces(self,
+                      faces: List[DetectedFace],
+                      sub_folders: List[Optional[str]],
+                      should_filter: List[bool]) -> List[DetectedFace]:
+        """ Filter the detected faces, either removing filtered faces from the list of detected
+        faces or setting the output subfolder to `"_identity"` for any filtered faces if saving
+        output is enabled.
+
+        Parameters
+        ----------
+        faces: list
+            List of detected face objects to filter out on size
+        sub_folders: list
+            List of subfolder locations for any faces that have already been filtered when
+            config option `save_filtered` has been enabled.
+        should_filter: list
+            List of 'bool' corresponding to face that have not already been marked for filtering.
+            ``True`` indicates face should be filtered, ``False`` indicates face should be kept
+
+        Returns
+        -------
+        detected_faces: list
+            The filtered list of detected face objects, if saving filtered faces has not been
+            selected or the full list of detected faces
+        """
+        retval: List[DetectedFace] = []
+        self._counts += sum(should_filter)
+        for idx, face in enumerate(faces):
+            fldr = sub_folders[idx]
+            if fldr is not None:
+                # Saving to sub folder is selected and face is already filtered
+                # so this face was excluded from identity check
+                retval.append(face)
+                continue
+            to_filter = should_filter.pop(0)
+            if not to_filter or self._save_output:
+                # Keep the face if not marked as filtered or we are to output to a subfolder
+                retval.append(face)
+            if to_filter and self._save_output:
+                sub_folders[idx] = "_identity"
+
+        return retval
+
+    def __call__(self,
+                 faces: List[DetectedFace],
+                 sub_folders: List[Optional[str]]) -> List[DetectedFace]:
+        """ Call the identity filter function
+
+        Parameters
+        ----------
+        faces: list
+            List of detected face objects to filter out on size
+        sub_folders: list
+            List of subfolder locations for any faces that have already been filtered when
+            config option `save_filtered` has been enabled.
+
+        Returns
+        -------
+        detected_faces: list
+            The filtered list of detected face objects, if saving filtered faces has not been
+            selected or the full list of detected faces
+        """
+        if not self._active:
+            return faces
+
+        identities = np.array([face.identity["vggface2"] for face, fldr in zip(faces, sub_folders)
+                               if fldr is None])
+        logger.trace("face_count: %s, already_filtered: %s, identity_shape: %s",  # type: ignore
+                     len(faces), sum(x is not None for x in sub_folders), identities.shape)
+
+        if not np.any(identities):
+            logger.trace("All faces already filtered: %s", sub_folders)  # type: ignore
+            return faces
+
+        should_filter: List[np.ndarray] = []
+        for f_type in get_args(Literal["filter", "nfilter"]):
+            if not getattr(self, f"_{f_type}_enabled"):
+                continue
+            should_filter.append(self._get_matches(f_type, identities))
+
+        # If any of the filter or nfilter evaluate to 'should filter' then filter out face
+        final_filter: List[bool] = np.array(should_filter).max(axis=0).tolist()
+        logger.trace("should_filter: %s, final_filter: %s",  # type: ignore
+                     should_filter, final_filter)
+        return self._filter_faces(faces, sub_folders, final_filter)
+
+    def output_counts(self):
+        """ Output the counts of filtered items """
+        if not self._active or not self._counts:
+            return
+        logger.info("Identity filtered: (%s)", self._counts)
--- a/scripts/extract.py
+++ b/scripts/extract.py
@@ -7,18 +7,22 @@ import logging
 import os
 import sys
 from argparse import Namespace
-from typing import List, Dict, Optional
+from typing import List, Dict, Optional, Tuple, TYPE_CHECKING, Union

+import numpy as np
 from tqdm import tqdm
+from lib.align.alignments import PNGHeaderDict

-from lib.image import encode_image, generate_thumbnail, ImagesLoader, ImagesSaver
+from lib.image import encode_image, generate_thumbnail, ImagesLoader, ImagesSaver, read_image_meta
 from lib.multithreading import MultiThread
 from lib.utils import get_folder, _image_extensions, _video_extensions
 from plugins.extract.pipeline import Extractor, ExtractMedia
 from scripts.fsmedia import Alignments, PostProcess, finalize

+if TYPE_CHECKING:
+    from lib.align.alignments import PNGHeaderAlignmentsDict

-tqdm.monitor_interval = 0  # workaround for TqdmSynchronisationWarning
+# tqdm.monitor_interval = 0  # workaround for TqdmSynchronisationWarning  # TODO?
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name


@@ -50,7 +54,9 @@ class Extract():  # pylint:disable=too-few-public-methods
        normalization = None if self._args.normalization == "none" else self._args.normalization
        maskers = ["components", "extended"]
        maskers += self._args.masker if self._args.masker else []
-        recognition = "vgg_face2" if arguments.identity else None
+        recognition = ("vgg_face2"
+                       if arguments.identity or arguments.filter or arguments.nfilter
+                       else None)
        self._extractor = Extractor(self._args.detector,
                                    self._args.aligner,
                                    maskers,
@@ -62,6 +68,10 @@ class Extract():  # pylint:disable=too-few-public-methods
                                    min_size=self._args.min_size,
                                    normalize_method=normalization,
                                    re_feed=self._args.re_feed)
+        self._filter = Filter(self._args.ref_threshold,
+                              self._args.filter,
+                              self._args.nfilter,
+                              self._extractor)

    def _get_input_locations(self) -> List[str]:
        """ Obtain the full path to input locations. Will be a list of locations if batch mode is
@@ -133,7 +143,7 @@ class Extract():  # pylint:disable=too-few-public-methods
        logger.debug("Returning output: '%s' for input: '%s'", retval, input_location)
        return retval

-    def process(self):
+    def process(self) -> None:
        """ The entry point for triggering the Extraction Process.

        Should only be called from  :class:`lib.cli.launcher.ScriptExecutor`
@@ -155,96 +165,349 @@ class Extract():  # pylint:disable=too-few-public-methods
            self._extractor.reset_phase_index()


-class _Extract():  # pylint:disable=too-few-public-methods
-    """ The Actual extraction process.
-
-    This class is called by the parent :class:`Extract` process
+class Filter():
+    """ Obtains and holds face identity embeddings for any filter/nfilter image files
+    passed in from the command line.

    Parameters
    ----------
+    filter_files: str, list or ``None``
+        The list of filter file(s) passed in as command line arguments
+    nfilter_files: str, list or ``None``
+        The list of nfilter file(s) passed in as command line arguments
    extractor: :class:`~plugins.extract.pipeline.Extractor`
-        The extractor pipeline for running extractions
-    arguments: :class:`argparse.Namespace`
-        The arguments to be passed to the extraction process as generated from Faceswap's command
-        line arguments
+        The extractor pipeline for obtaining face identity from images
    """
    def __init__(self,
-                 extractor: Extractor,
-                 arguments: Namespace) -> None:
-        logger.debug("Initializing %s: (extractor: %s, args: %s)", self.__class__.__name__,
-                     extractor, arguments)
-        self._args = arguments
-        self._output_dir = None if self._args.skip_saving_faces else get_folder(
-            self._args.output_dir)
+                 threshold: float,
+                 filter_files: Optional[Union[str, List[str]]],
+                 nfilter_files: Optional[Union[str, List[str]]],
+                 extractor: Extractor) -> None:
+        logger.debug("Initializing %s: (threshold: %s, filter_files: %s, nfilter_files: %s "
+                     "extractor: %s)", self.__class__.__name__, threshold, filter_files,
+                     nfilter_files, extractor)
+        self._threshold = threshold
+        self._filter_files, self._nfilter_files = self._validate_inputs(filter_files,
+                                                                        nfilter_files)

-        logger.info("Output Directory: %s", self._output_dir)
-        self._images = ImagesLoader(self._args.input_dir, fast_count=True)
-        self._alignments = Alignments(self._args, True, self._images.is_video)
+        if not self._filter_files and not self._nfilter_files:
+            logger.debug("Filter not selected. Exiting %s", self.__class__.__name__)
+            return
+
+        self._embeddings: List[np.ndarray] = [np.array([]) for _ in self._filter_files]
+        self._nembeddings: List[np.ndarray] = [np.array([]) for _ in self._nfilter_files]
        self._extractor = extractor

-        self._existing_count = 0
-        self._set_skip_list()
-
-        self._post_process = PostProcess(arguments)
-        self._threads: List[MultiThread] = []
-        self._verify_output = False
+        self._get_embeddings()
+        self._extractor.recognition.add_identity_filters(self.embeddings,
+                                                         self.n_embeddings,
+                                                         self._threshold)
        logger.debug("Initialized %s", self.__class__.__name__)

    @property
-    def _save_interval(self) -> Optional[int]:
-        """ int: The number of frames to be processed between each saving of the alignments file if
-        it has been provided, otherwise ``None`` """
-        if hasattr(self._args, "save_interval"):
-            return self._args.save_interval
-        return None
+    def active(self):
+        """ bool: ``True`` if filter files have been passed in command line arguments. ``False`` if
+        no filter files have been provided """
+        return bool(self._filter_files) or bool(self._nfilter_files)

    @property
-    def _skip_num(self) -> int:
-        """ int: Number of frames to skip if extract_every_n has been provided """
-        return self._args.extract_every_n if hasattr(self._args, "extract_every_n") else 1
+    def embeddings(self) -> np.ndarray:
+        """ :class:`numpy.ndarray`: The filter embeddings"""
+        if self._embeddings and all(np.any(e) for e in self._embeddings):
+            retval = np.concatenate(self._embeddings, axis=0)
+        else:
+            retval = np.array([])
+        return retval

-    def _set_skip_list(self) -> None:
-        """ Add the skip list to the image loader
+    @property
+    def n_embeddings(self) -> np.ndarray:
+        """ :class:`numpy.ndarray`: The n-filter embeddings"""
+        if self._nembeddings and all(np.any(e) for e in self._nembeddings):
+            retval = np.concatenate(self._nembeddings, axis=0)
+        else:
+            retval = np.array([])
+        return retval

-        Checks against `extract_every_n` and the existence of alignments data (can exist if
-        `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame
-        indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.
+    @classmethod
+    def _validate_inputs(cls,
+                         filter_files: Optional[Union[str, List[str]]],
+                         nfilter_files: Optional[Union[str, List[str]]]) -> Tuple[List[str],
+                                                                                  List[str]]:
+        """ Validates that the given filter/nfilter files exist, are image files and are unique
+
+        Parameters
+        ----------
+        filter_files: str, list or ``None``
+            The list of filter file(s) passed in as command line arguments
+        nfilter_files: str, list or ``None``
+            The list of nfilter file(s) passed in as command line arguments
+
+        Returns
+        -------
+        filter_files: list
+            List of full paths to filter files
+        nfilter_files: list
+            List of full paths to nfilter files
        """
-        if self._skip_num == 1 and not self._alignments.data:
-            logger.debug("No frames to be skipped")
+        error = False
+        retval: List[List[str]] = []
+        for files in (filter_files, nfilter_files):
+            filt_files = [files] if isinstance(files, str) else files
+            filt_files = [] if filt_files is None else filt_files
+            for file in filt_files:
+                if (not os.path.isfile(file) or
+                        os.path.splitext(file)[-1].lower() not in _image_extensions):
+                    logger.warning("Filter file '%s' does not exist or is not an image file", file)
+                    error = True
+            retval.append(filt_files)
+
+        filters = retval[0]
+        nfilters = retval[1]
+        f_fnames = set(os.path.basename(fname) for fname in filters)
+        n_fnames = set(os.path.basename(fname) for fname in nfilters)
+        if f_fnames.intersection(n_fnames):
+            error = True
+            logger.warning("filter and nfilter filenames should be unique. The following "
+                           "filenames exist in both folders: %s", f_fnames.intersection(n_fnames))
+
+        if error:
+            logger.error("There was a problem processing filter files. See the above warnings for "
+                         "details")
+            sys.exit(1)
+        logger.debug("filter_files: %s, nfilter_files: %s", retval[0], retval[1])
+
+        return filters, nfilters
+
+    @classmethod
+    def _identity_from_extracted(cls, filename) -> Tuple[np.ndarray, bool]:
+        """ Test whether the given image is a faceswap extracted face and contains identity
+        information. If so, return the identity embedding
+
+        Parameters
+        ----------
+        filename: str
+            Full path to the image file to load
+
+        Returns
+        -------
+        :class:`numpy.ndarray`
+            The identity embeddings, if they can be obtained from the image header, otherwise an
+            empty array
+        bool
+            ``True`` if the image is a faceswap extracted image otherwise ``False``
+        """
+        if os.path.splitext(filename)[-1].lower() != ".png":
+            logger.info("'%s' not a png. Returning empty array", filename)
+            return np.array([]), False
+
+        meta = read_image_meta(filename)
+        if "itxt" not in meta or "alignments" not in meta["itxt"]:
+            logger.debug("'%s' does not contain faceswap data. Returning empty array", filename)
+            return np.array([]), False
+
+        align: "PNGHeaderAlignmentsDict" = meta["itxt"]["alignments"]
+        if "identity" not in align or "vggface2" not in align["identity"]:
+            logger.debug("'%s' does not contain identity data. Returning empty array", filename)
+            return np.array([]), True
+
+        retval = np.array(align["identity"]["vggface2"])
+        logger.debug("Obtained identity for '%s'. Shape: %s", filename, retval.shape)
+
+        return retval, True
+
+    def _process_extracted(self, item: ExtractMedia) -> None:
+        """ Process the output from the extraction pipeline.
+
+        If no face has been detected, or multiple faces are detected for the inclusive filter,
+        embeddings and filenames are removed from the filter.
+
+        if a single face is detected or multiple faces are detected for the exclusive filter,
+        embeddings are added to the relevent filter list
+
+        Parameters
+        ----------
+        item: :class:`plugins.extract.Pipeline.ExtracMedia`
+            The output from the extraction pipeline containing the identity encodings
+        """
+        is_filter = item.filename in self._filter_files
+        lbl = "filter" if is_filter else "nfilter"
+        filelist = self._filter_files if is_filter else self._nfilter_files
+        embeddings = self._embeddings if is_filter else self._nembeddings
+        identities = np.array([face.identity["vggface2"] for face in item.detected_faces])
+        idx = filelist.index(item.filename)
+
+        if len(item.detected_faces) == 0:
+            logger.warning("No faces detected for %s in file '%s'. Image will not be used",
+                           lbl, os.path.basename(item.filename))
+            filelist.pop(idx)
+            embeddings.pop(idx)
            return
-        skip_list = []
-        for idx, filename in enumerate(self._images.file_list):
-            if idx % self._skip_num != 0:
-                logger.trace("Adding image '%s' to skip list due to "  # type: ignore
-                             "extract_every_n = %s", filename, self._skip_num)
-                skip_list.append(idx)
-            # Items may be in the alignments file if skip-existing[-faces] is selected
-            elif os.path.basename(filename) in self._alignments.data:
-                self._existing_count += 1
-                logger.trace("Removing image: '%s' due to previously existing",  # type: ignore
-                             filename)
-                skip_list.append(idx)
-        if self._existing_count != 0:
-            logger.info("Skipping %s frames due to skip_existing/skip_existing_faces.",
-                        self._existing_count)
-        logger.debug("Adding skip list: %s", skip_list)
+
+        if len(item.detected_faces) == 1:
+            logger.debug("Adding identity for %s from file '%s'", lbl, item.filename)
+            embeddings[idx] = identities
+            return
+
+        if len(item.detected_faces) > 1 and is_filter:
+            logger.warning("%s faces detected for filter in '%s'. These identies will not be used",
+                           len(item.detected_faces), os.path.basename(item.filename))
+            filelist.pop(idx)
+            embeddings.pop(idx)
+            return
+
+        if len(item.detected_faces) > 1 and not is_filter:
+            logger.warning("%s faces detected for nfilter in '%s'. All of these identies will be "
+                           "used", len(item.detected_faces), os.path.basename(item.filename))
+            embeddings[idx] = identities
+            return
+
+    def _identity_from_extractor(self, file_list: List[str], aligned: List[str]) -> None:
+        """ Obtain the identity embeddings from the extraction pipeline
+
+        Parameters
+        ----------
+        filesile_list: list
+            List of full path to images to run through the extraction pipeline
+        aligned: list
+            List of full path to images that exist in attr:`filelist` that are faceswap aligned
+            images
+        """
+        logger.info("Extracting faces to obtain identity from images")
+        logger.debug("Files requiring full extraction: %s",
+                     [fname for fname in file_list if fname not in aligned])
+        logger.debug("Aligned files requiring identity info: %s", aligned)
+
+        loader = PipelineLoader(file_list, self._extractor, aligned_filenames=aligned)
+        loader.launch()
+
+        for phase in range(self._extractor.passes):
+            is_final = self._extractor.final_pass
+            detected_faces: Dict[str, ExtractMedia] = {}
+            self._extractor.launch()
+            desc = "Obtaining reference face Identity"
+            if self._extractor.passes > 1:
+                desc = (f"{desc } pass {phase + 1} of {self._extractor.passes}: "
+                        f"{self._extractor.phase_text}")
+            for extract_media in tqdm(self._extractor.detected_faces(),
+                                      total=len(file_list),
+                                      file=sys.stdout,
+                                      desc=desc):
+                if is_final:
+                    self._process_extracted(extract_media)
+                else:
+                    extract_media.remove_image()
+                    # cache extract_media for next run
+                    detected_faces[extract_media.filename] = extract_media
+
+            if not is_final:
+                logger.debug("Reloading images")
+                loader.reload(detected_faces)
+
+        self._extractor.reset_phase_index()
+
+    def _get_embeddings(self) -> None:
+        """ Obtain the embeddings for the given filter lists """
+        needs_extraction: List[str] = []
+        aligned: List[str] = []
+
+        for files, embed in zip((self._filter_files, self._nfilter_files),
+                                (self._embeddings, self._nembeddings)):
+            for idx, file in enumerate(files):
+                identity, is_aligned = self._identity_from_extracted(file)
+                if np.any(identity):
+                    logger.debug("Obtained identity from png header: '%s'", file)
+                    embed[idx] = identity[None, ...]
+                    continue
+
+                needs_extraction.append(file)
+                if is_aligned:
+                    aligned.append(file)
+
+        if needs_extraction:
+            self._identity_from_extractor(needs_extraction, aligned)
+
+        if not self._nfilter_files and not self._filter_files:
+            logger.error("No faces were detected from your selected identity filter files")
+            sys.exit(1)
+
+        logger.debug("Filter: (filenames: %s, shape: %s), nFilter: (filenames: %s, shape: %s)",
+                     [os.path.basename(f) for f in self._filter_files],
+                     self.embeddings.shape,
+                     [os.path.basename(f) for f in self._nfilter_files],
+                     self.n_embeddings.shape)
+
+
+class PipelineLoader():
+    """ Handles loading and reloading images into the extraction pipeline.
+
+    Parameters
+    ----------
+    path: str or list of str
+        Full path to a folder of images or a video file or a list of image files
+    extractor: :class:`~plugins.extract.pipeline.Extractor`
+        The extractor pipeline for obtaining face identity from images
+    aligned_filenames: list, optional
+        Used for when the loader is used for getting face filter embeddings. List of full path to
+        image files that exist in :attr:`path` that are aligned faceswap images
+    """
+    def __init__(self,
+                 path: Union[str, List[str]],
+                 extractor: Extractor,
+                 aligned_filenames: Optional[List[str]] = None) -> None:
+        logger.debug("Initializing %s: (path: %s, extractor: %s, aligned_filenames: %s)",
+                     self.__class__.__name__, path, extractor, aligned_filenames)
+        self._images = ImagesLoader(path, fast_count=True)
+        self._extractor = extractor
+        self._threads: List[MultiThread] = []
+        self._aligned_filenames = [] if aligned_filenames is None else aligned_filenames
+        logger.debug("Initialized %s", self.__class__.__name__)
+
+    @property
+    def is_video(self) -> bool:
+        """ bool: ``True`` if the input location is a video file, ``False`` if it is a folder of
+        images """
+        return self._images.is_video
+
+    @property
+    def file_list(self) -> List[str]:
+        """ list: A full list of files in the source location. If the input is a video
+        then this is a list of dummy filenames as corresponding to an alignments file """
+        return self._images.file_list
+
+    @property
+    def process_count(self) -> int:
+        """ int: The number of images or video frames to be processed (IE the total count less
+        items that are to be skipped from the :attr:`skip_list`)"""
+        return self._images.process_count
+
+    def add_skip_list(self, skip_list: List[int]) -> None:
+        """ Add a skip list to the :class:`ImagesLoader`
+
+        Parameters
+        ----------
+        skip_list: list
+            A list of indices corresponding to the frame indices that should be skipped by the
+            :func:`load` function.
+        """
        self._images.add_skip_list(skip_list)

-    def process(self) -> None:
-        """ The entry point for triggering the Extraction Process.
-
-        Should only be called from  :class:`lib.cli.launcher.ScriptExecutor`
-        """
-        # from lib.queue_manager import queue_manager ; queue_manager.debug_monitor(3)
+    def launch(self) -> None:
+        """ Launch the image loading pipeline """
        self._threaded_redirector("load")
-        self._run_extraction()
+
+    def reload(self, detected_faces: Dict[str, ExtractMedia]) -> None:
+        """ Reload images for multiple pipeline passes """
+        self._threaded_redirector("reload", (detected_faces, ))
+
+    def check_thread_error(self) -> None:
+        """ Check if any errors have occurred in the running threads and raise their errors """
+        for thread in self._threads:
+            thread.check_and_raise_error()
+
+    def join(self) -> None:
+        """ Join all open loader threads """
        for thread in self._threads:
            thread.join()
-        self._alignments.save()
-        finalize(self._images.process_count + self._existing_count,
-                 self._alignments.faces_count,
-                 self._verify_output)

    def _threaded_redirector(self, task: str, io_args: Optional[tuple] = None) -> None:
        """ Redirect image input/output tasks to relevant queues in background thread
@@ -275,7 +538,8 @@ class _Extract():  # pylint:disable=too-few-public-methods
            if load_queue.shutdown.is_set():
                logger.debug("Load Queue: Stop signal received. Terminating")
                break
-            item = ExtractMedia(filename, image[..., :3])
+            is_aligned = filename in self._aligned_filenames
+            item = ExtractMedia(filename, image[..., :3], is_aligned=is_aligned)
            load_queue.put(item)
        load_queue.put("EOF")
        logger.debug("Load Images: Complete")
@@ -308,6 +572,97 @@ class _Extract():  # pylint:disable=too-few-public-methods
        load_queue.put("EOF")
        logger.debug("Reload Images: Complete")

+
+class _Extract():  # pylint:disable=too-few-public-methods
+    """ The Actual extraction process.
+
+    This class is called by the parent :class:`Extract` process
+
+    Parameters
+    ----------
+    extractor: :class:`~plugins.extract.pipeline.Extractor`
+        The extractor pipeline for running extractions
+    arguments: :class:`argparse.Namespace`
+        The arguments to be passed to the extraction process as generated from Faceswap's command
+        line arguments
+    """
+    def __init__(self,
+                 extractor: Extractor,
+                 arguments: Namespace) -> None:
+        logger.debug("Initializing %s: (extractor: %s, args: %s)", self.__class__.__name__,
+                     extractor, arguments)
+        self._args = arguments
+        self._output_dir = None if self._args.skip_saving_faces else get_folder(
+            self._args.output_dir)
+
+        logger.info("Output Directory: %s", self._output_dir)
+        self._loader = PipelineLoader(self._args.input_dir, extractor)
+
+        self._alignments = Alignments(self._args, True, self._loader.is_video)
+        self._extractor = extractor
+
+        self._existing_count = 0
+        self._set_skip_list()
+
+        self._post_process = PostProcess(arguments)
+        self._verify_output = False
+        logger.debug("Initialized %s", self.__class__.__name__)
+
+    @property
+    def _save_interval(self) -> Optional[int]:
+        """ int: The number of frames to be processed between each saving of the alignments file if
+        it has been provided, otherwise ``None`` """
+        if hasattr(self._args, "save_interval"):
+            return self._args.save_interval
+        return None
+
+    @property
+    def _skip_num(self) -> int:
+        """ int: Number of frames to skip if extract_every_n has been provided """
+        return self._args.extract_every_n if hasattr(self._args, "extract_every_n") else 1
+
+    def _set_skip_list(self) -> None:
+        """ Add the skip list to the image loader
+
+        Checks against `extract_every_n` and the existence of alignments data (can exist if
+        `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame
+        indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.
+        """
+        if self._skip_num == 1 and not self._alignments.data:
+            logger.debug("No frames to be skipped")
+            return
+        skip_list = []
+        for idx, filename in enumerate(self._loader.file_list):
+            if idx % self._skip_num != 0:
+                logger.trace("Adding image '%s' to skip list due to "  # type: ignore
+                             "extract_every_n = %s", filename, self._skip_num)
+                skip_list.append(idx)
+            # Items may be in the alignments file if skip-existing[-faces] is selected
+            elif os.path.basename(filename) in self._alignments.data:
+                self._existing_count += 1
+                logger.trace("Removing image: '%s' due to previously existing",  # type: ignore
+                             filename)
+                skip_list.append(idx)
+        if self._existing_count != 0:
+            logger.info("Skipping %s frames due to skip_existing/skip_existing_faces.",
+                        self._existing_count)
+        logger.debug("Adding skip list: %s", skip_list)
+        self._loader.add_skip_list(skip_list)
+
+    def process(self) -> None:
+        """ The entry point for triggering the Extraction Process.
+
+        Should only be called from  :class:`lib.cli.launcher.ScriptExecutor`
+        """
+        # from lib.queue_manager import queue_manager ; queue_manager.debug_monitor(3)
+        self._loader.launch()
+        self._run_extraction()
+        self._loader.join()
+        self._alignments.save()
+        finalize(self._loader.process_count + self._existing_count,
+                 self._alignments.faces_count,
+                 self._verify_output)
+
    def _run_extraction(self) -> None:
        """ The main Faceswap Extraction process

@@ -318,23 +673,19 @@ class _Extract():  # pylint:disable=too-few-public-methods
        size = self._args.size if hasattr(self._args, "size") else 256
        saver = None if self._args.skip_saving_faces else ImagesSaver(self._output_dir,
                                                                      as_bytes=True)
-        exception = False
-
        for phase in range(self._extractor.passes):
-            if exception:
-                break
            is_final = self._extractor.final_pass
-            detected_faces = {}
+            detected_faces: Dict[str, ExtractMedia] = {}
            self._extractor.launch()
-            self._check_thread_error()
+            self._loader.check_thread_error()
            ph_desc = "Extraction" if self._extractor.passes == 1 else self._extractor.phase_text
            desc = f"Running pass {phase + 1} of {self._extractor.passes}: {ph_desc}"
            for idx, extract_media in enumerate(tqdm(self._extractor.detected_faces(),
-                                                     total=self._images.process_count,
+                                                     total=self._loader.process_count,
                                                     file=sys.stdout,
                                                     desc=desc,
                                                     leave=False)):
-                self._check_thread_error()
+                self._loader.check_thread_error()
                if is_final:
                    self._output_processing(extract_media, size)
                    self._output_faces(saver, extract_media)
@@ -347,15 +698,10 @@ class _Extract():  # pylint:disable=too-few-public-methods

            if not is_final:
                logger.debug("Reloading images")
-                self._threaded_redirector("reload", (detected_faces, ))
+                self._loader.reload(detected_faces)
        if saver is not None:
            saver.close()

-    def _check_thread_error(self) -> None:
-        """ Check if any errors have occurred in the running threads and their errors """
-        for thread in self._threads:
-            thread.check_and_raise_error()
-
    def _output_processing(self, extract_media: ExtractMedia, size: int) -> None:
        """ Prepare faces for output

@@ -408,14 +754,17 @@ class _Extract():  # pylint:disable=too-few-public-methods
        for face_id, face in enumerate(extract_media.detected_faces):
            real_face_id = face_id - skip_idx
            output_filename = f"{filename}_{real_face_id}{extension}"
-            meta = dict(alignments=face.to_png_meta(),
-                        source=dict(alignments_version=self._alignments.version,
-                                    original_filename=output_filename,
-                                    face_index=real_face_id,
-                                    source_filename=os.path.basename(extract_media.filename),
-                                    source_is_video=self._images.is_video,
-                                    source_frame_dims=extract_media.image_size))
-            image = encode_image(face.aligned.face, extension, metadata=meta)
+            aligned = face.aligned.face
+            assert aligned is not None
+            meta: PNGHeaderDict = dict(
+                alignments=face.to_png_meta(),
+                source=dict(alignments_version=self._alignments.version,
+                            original_filename=output_filename,
+                            face_index=real_face_id,
+                            source_filename=os.path.basename(extract_media.filename),
+                            source_is_video=self._loader.is_video,
+                            source_frame_dims=extract_media.image_size))
+            image = encode_image(aligned, extension, metadata=meta)

            sub_folder = extract_media.sub_folders[face_id]
            # Binned faces shouldn't risk filename clash, so just use original id
--- a/scripts/fsmedia.py
+++ b/scripts/fsmedia.py
@@ -16,15 +16,9 @@ import numpy as np
 import imageio

 from lib.align import Alignments as AlignmentsBase, get_centered_size
-from lib.face_filter import FaceFilter as FilterFunc
 from lib.image import count_frames, read_image
 from lib.utils import (camel_case_split, get_image_paths, _video_extensions)

-if sys.version_info < (3, 8):
-    from typing_extensions import get_args, Literal
-else:
-    from typing import get_args, Literal
-
 if TYPE_CHECKING:
    from argparse import Namespace
    from lib.align import AlignedFace
@@ -408,33 +402,6 @@ class PostProcess():  # pylint:disable=too-few-public-methods
        if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
            postprocess_items["DebugLandmarks"] = None

-        # Face Filter post processing
-        if ((hasattr(self._args, "filter") and self._args.filter is not None) or
-                (hasattr(self._args, "nfilter") and
-                 self._args.nfilter is not None)):
-
-            if hasattr(self._args, "detector"):
-                detector = self._args.detector.replace("-", "_").lower()
-            else:
-                detector = "cv2_dnn"
-            if hasattr(self._args, "aligner"):
-                aligner = self._args.aligner.replace("-", "_").lower()
-            else:
-                aligner = "cv2_dnn"
-
-            face_filter = dict(detector=detector,
-                               aligner=aligner,
-                               multiprocess=not self._args.singleprocess)
-            filter_lists = {}
-            if hasattr(self._args, "ref_threshold"):
-                face_filter["ref_threshold"] = self._args.ref_threshold
-            for filter_type in ('filter', 'nfilter'):
-                filter_args = getattr(self._args, filter_type, None)
-                filter_args = None if not filter_args else filter_args
-                filter_lists[filter_type] = filter_args
-            face_filter["filter_lists"] = filter_lists
-            postprocess_items["FaceFilter"] = {"kwargs": face_filter}
-
        logger.debug("Postprocess Items: %s", postprocess_items)
        return postprocess_items

@@ -642,130 +609,3 @@ class DebugLandmarks(PostProcessAction):  # pylint: disable=too-few-public-metho
            roi = face.aligned.get_cropped_roi(face.aligned.size, self._legacy_size, "legacy")
            cv2.rectangle(face.aligned.face, tuple(roi[:2]), tuple(roi[2:]), (0, 0, 255), 1)
            self._print_stats(face.aligned)
-
-
-class FaceFilter(PostProcessAction):
-    """ Filter in or out faces based on input image(s). Extract or Convert
-
-    Parameters
-    -----------
-    args: tuple
-        Unused
-    kwargs: dict
-        Keyword arguments for face filter:
-
-        * **detector** (`str`) - The detector to use
-
-        * **aligner** (`str`) - The aligner to use
-
-        * **multiprocess** (`bool`) - Whether to run the extraction pipeline in single process \
-        mode or not
-
-        * **ref_threshold** (`float`) - The reference threshold for a positive match
-
-        * **filter_lists** (`dict`) - The filter and nfilter image paths
-    """
-
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-        logger.info("Extracting and aligning face for Face Filter...")
-        self._filter = self._load_face_filter(**kwargs)
-        logger.debug("Initialized %s", self.__class__.__name__)
-
-    def _load_face_filter(self,
-                          filter_lists: Dict[str, str],
-                          ref_threshold: float,
-                          aligner: str,
-                          detector: str,
-                          multiprocess: bool) -> Optional[FilterFunc]:
-        """ Set up and load the :class:`~lib.face_filter.FaceFilter`.
-
-        Parameters
-        ----------
-        filter_lists: dict
-            The filter and nfilter image paths
-        ref_threshold: float
-            The reference threshold for a positive match
-        aligner: str
-            The aligner to use
-        detector: str
-            The detector to use
-        multiprocess: bool
-            Whether to run the extraction pipeline in single process mode or not
-
-        Returns
-        -------
-        :class:`~lib.face_filter.FaceFilter`
-            The face filter
-        """
-        if not any(val for val in filter_lists.values()):
-            return None
-
-        facefilter = None
-        filter_files = [self._set_face_filter(f_type, filter_lists[f_type])
-                        for f_type in get_args(Literal["filter", "nfilter"])]
-
-        if any(filters for filters in filter_files):
-            facefilter = FilterFunc(filter_files[0],
-                                    filter_files[1],
-                                    detector,
-                                    aligner,
-                                    multiprocess,
-                                    ref_threshold)
-            logger.debug("Face filter: %s", facefilter)
-        else:
-            self._valid = False
-        return facefilter
-
-    @classmethod
-    def _set_face_filter(cls,
-                         f_type: Literal["filter", "nfilter"],
-                         f_args: Union[str, List[str]]) -> List[str]:
-        """ Check filter files exist and add the filter file paths to a list.
-
-        Parameters
-        ----------
-        f_type: {"filter", "nfilter"}
-            The type of filter to create this list for
-        f_args: str or list
-            The filter image(s) to use
-
-        Returns
-        -------
-        list
-            The confirmed existing paths to filter files to use
-        """
-        if not f_args:
-            return []
-
-        logger.info("%s: %s", f_type.title(), f_args)
-        filter_files = f_args if isinstance(f_args, list) else [f_args]
-        filter_files = [fpath for fpath in filter_files if os.path.exists(fpath)]
-        if not filter_files:
-            logger.warning("Face %s files were requested, but no files could be found. This "
-                           "filter will not be applied.", f_type)
-        logger.debug("Face Filter files: %s", filter_files)
-        return filter_files
-
-    def process(self, extract_media: "ExtractMedia") -> None:
-        """ Filters in or out any wanted or unwanted faces based on command line arguments.
-
-        Parameters
-        ----------
-        extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
-            The :class:`~plugins.extract.pipeline.ExtractMedia` object to perform the
-            face filtering on.
-        """
-        if not self._filter:
-            return
-        ret_faces = []
-        for idx, detect_face in enumerate(extract_media.detected_faces):
-            check_item = detect_face["face"] if isinstance(detect_face, dict) else detect_face
-            if not self._filter.check(extract_media.image, check_item):
-                logger.verbose("Skipping not recognized face: (Frame: %s Face %s)",  # type: ignore
-                               extract_media.filename, idx)
-                continue
-            logger.trace("Accepting recognised face. Frame: %s. Face: %s",  # type: ignore
-                         extract_media.filename, idx)
-            ret_faces.append(detect_face)
-        extract_media.add_detected_faces(ret_faces)
--- a/tools/manual/manual.py
+++ b/tools/manual/manual.py
@@ -801,7 +801,8 @@ class Aligner():
        for plugin, aligner in self._aligners.items():
            if plugin == "mask":
                continue
-            aligner.set_aligner_normalization_method(method)
+            logger.debug("Setting to: '%s'", method)
+            aligner.aligner.set_normalize_method(method)


 class FrameLoader():
--- a/tools/mask/mask.py
+++ b/tools/mask/mask.py
@@ -19,7 +19,7 @@ from plugins.extract.pipeline import Extractor, ExtractMedia
 if TYPE_CHECKING:
    from argparse import Namespace
    from lib.align.aligned_face import CenteringType
-    from lib.align.alignments import AlignmentFileDict
+    from lib.align.alignments import AlignmentFileDict, PNGHeaderDict, PNGHeaderSourceDict
    from lib.queue_manager import EventQueue

 logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
@@ -125,9 +125,7 @@ class Mask():  # pylint:disable=too-few-public-methods
            logger.debug("Update type `output` selected. Not launching extractor")
            return None
        logger.debug("masker: %s", self._mask_type)
-        extractor = Extractor(None, None, self._mask_type,
-                              exclude_gpus=exclude_gpus,
-                              image_is_aligned=self._input_is_faces)
+        extractor = Extractor(None, None, self._mask_type, exclude_gpus=exclude_gpus)
        extractor.launch()
        logger.debug(extractor)
        return extractor
@@ -172,7 +170,7 @@ class Mask():  # pylint:disable=too-few-public-methods
    def _process_face(self,
                      filename: str,
                      image: np.ndarray,
-                      metadata: Dict[str, Any]) -> Optional["ExtractMedia"]:
+                      metadata: "PNGHeaderDict") -> Optional["ExtractMedia"]:
        """ Process a single face when masking from face images

        filename: str
@@ -190,12 +188,12 @@ class Mask():  # pylint:disable=too-few-public-methods
        """
        frame_name = metadata["source"]["source_filename"]
        face_index = metadata["source"]["face_index"]
-        alignment = self._alignments.get_faces_in_frame(frame_name)
-        if not alignment or face_index > len(alignment) - 1:
+        alignments = self._alignments.get_faces_in_frame(frame_name)
+        if not alignments or face_index > len(alignments) - 1:
            self._counts["skip"] += 1
            logger.warning("Skipping Face not found in alignments file: '%s'", filename)
            return None
-        alignment = alignment[face_index]
+        alignment = alignments[face_index]
        self._counts["face"] += 1

        if self._check_for_missing(frame_name, face_index, alignment):
@@ -207,7 +205,7 @@ class Mask():  # pylint:disable=too-few-public-methods
            self._save(frame_name, face_index, detected_face)
            return None

-        media = ExtractMedia(filename, image, detected_faces=[detected_face])
+        media = ExtractMedia(filename, image, detected_faces=[detected_face], is_aligned=True)
        media.add_frame_metadata(metadata["source"])
        self._counts["update"] += 1
        return media
@@ -236,7 +234,7 @@ class Mask():  # pylint:disable=too-few-public-methods
                    logger.warning("Legacy face not found in alignments file. This face has not "
                                   "been updated: '%s'", filename)
                    continue
-            if "source_frame_dims" not in metadata["source"]:
+            if not metadata.get("source_frame_dims"):
                logger.error("The faces need to be re-extracted as at least some of them do not "
                             "contain information required to correctly generate masks.")
                logger.error("You can re-extract the face-set by using the Alignments Tool's "
@@ -404,8 +402,8 @@ class Mask():  # pylint:disable=too-few-public-methods
                         frame_name, face_index)

            self._alignments.update_face(frame_name, face_index, face.to_alignment())
-            metadata = dict(alignments=face.to_png_meta(),
-                            source=extractor_output.frame_metadata)
+            metadata: "PNGHeaderDict" = dict(alignments=face.to_png_meta(),
+                                             source=extractor_output.frame_metadata)
            self._faces_saver.save(extractor_output.filename,
                                   encode_image(extractor_output.image, ".png", metadata=metadata))