Use SVC to normalize and classify faces for recognition (#14835)

* Add margin to detected faces for embeddings

* Standardize pixel values for face input

* Use SVC to classify faces

* Clear classifier when new face is added

* Formatting

* Add dependency

docker/main/requirements-wheels.txt

@@ -11,9 +11,7 @@ markupsafe == 2.1.*
 python-multipart == 0.0.12
 # General
 mypy == 1.6.1
-numpy == 1.26.*
 onvif_zeep == 0.2.12
-opencv-python-headless == 4.9.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
@@ -27,11 +25,15 @@ ruamel.yaml == 0.18.*
 tzlocal == 5.2
 requests == 2.32.*
 types-requests == 2.32.*
-scipy == 1.13.*
 norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
+# Image Manipulation
+numpy == 1.26.*
+opencv-python-headless == 4.9.0.*
+scipy == 1.13.*
+scikit-learn == 1.5.*
 # OpenVino & ONNX
 openvino == 2024.3.*
 onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'

frigate/embeddings/functions/onnx.py

@@ -221,6 +221,9 @@ class GenericONNXEmbedding:
             # copy img image into center of result image
             frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
 
+            # standardize pixel values across channels
+            mean, std = frame.mean(), frame.std()
+            frame = (frame - mean) / std
             frame = np.expand_dims(frame, axis=0)
             return [{"input_2": frame}]
         elif self.model_type == ModelTypeEnum.lpr_detect:

frigate/embeddings/maintainer.py

@@ -29,12 +29,12 @@ from frigate.genai import get_genai_client
 from frigate.models import Event
 from frigate.util.builtin import serialize
 from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
+from frigate.util.model import FaceClassificationModel
 
 from .embeddings import Embeddings
 
 logger = logging.getLogger(__name__)
 
-REQUIRED_FACES = 2
 MAX_THUMBNAILS = 10
 
 
@@ -67,6 +67,9 @@ class EmbeddingMaintainer(threading.Thread):
         self.face_recognition_enabled = self.config.face_recognition.enabled
         self.requires_face_detection = "face" not in self.config.objects.all_objects
         self.detected_faces: dict[str, float] = {}
+        self.face_classifier = (
+            FaceClassificationModel(db) if self.face_recognition_enabled else None
+        )
 
         # create communication for updating event descriptions
         self.requestor = InterProcessRequestor()
@@ -137,13 +140,15 @@ class EmbeddingMaintainer(threading.Thread):
                         self.embeddings.text_embedding([data])[0], pack=False
                     )
                 elif topic == EmbeddingsRequestEnum.register_face.value:
+                    if not self.face_recognition_enabled:
+                        return False
+
                     if data.get("cropped"):
                         self.embeddings.embed_face(
                             data["face_name"],
                             base64.b64decode(data["image"]),
                             upsert=True,
                         )
-                        return True
                     else:
                         img = cv2.imdecode(
                             np.frombuffer(
@@ -164,7 +169,8 @@ class EmbeddingMaintainer(threading.Thread):
                             data["face_name"], webp.tobytes(), upsert=True
                         )
 
-                    return False
+                self.face_classifier.clear_classifier()
+                return True
             except Exception as e:
                 logger.error(f"Unable to handle embeddings request {e}")
 
@@ -336,18 +342,6 @@ class EmbeddingMaintainer(threading.Thread):
         if event_id:
             self.handle_regenerate_description(event_id, source)
 
-    def _search_face(self, query_embedding: bytes) -> list[tuple[str, float]]:
-        """Search for the face most closely matching the embedding."""
-        sql_query = f"""
-            SELECT
-                id,
-                distance
-            FROM vec_faces
-            WHERE face_embedding MATCH ?
-                AND k = {REQUIRED_FACES} ORDER BY distance
-        """
-        return self.embeddings.db.execute_sql(sql_query, [query_embedding]).fetchall()
-
     def _detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]:
         """Detect faces in input image."""
         self.face_detector.setInputSize((input.shape[1], input.shape[0]))
@@ -400,13 +394,21 @@ class EmbeddingMaintainer(threading.Thread):
             rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
             left, top, right, bottom = person_box
             person = rgb[top:bottom, left:right]
-            face = self._detect_face(person)
+            face_box = self._detect_face(person)
 
-            if not face:
+            if not face_box:
                 logger.debug("Detected no faces for person object.")
                 return
 
-            face_frame = person[face[1] : face[3], face[0] : face[2]]
+            margin = int((face_box[2] - face_box[0]) * 0.25)
+            face_frame = person[
+                max(0, face_box[1] - margin) : min(
+                    frame.shape[0], face_box[3] + margin
+                ),
+                max(0, face_box[0] - margin) : min(
+                    frame.shape[1], face_box[2] + margin
+                ),
+            ]
             face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
         else:
             # don't run for object without attributes
@@ -434,8 +436,15 @@ class EmbeddingMaintainer(threading.Thread):
                 return
 
             face_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
+            margin = int((face_box[2] - face_box[0]) * 0.25)
+
             face_frame = face_frame[
-                face_box[1] : face_box[3], face_box[0] : face_box[2]
+                max(0, face_box[1] - margin) : min(
+                    frame.shape[0], face_box[3] + margin
+                ),
+                max(0, face_box[0] - margin) : min(
+                    frame.shape[1], face_box[2] + margin
+                ),
             ]
 
         ret, webp = cv2.imencode(
@@ -446,34 +455,23 @@ class EmbeddingMaintainer(threading.Thread):
             logger.debug("Not processing face due to error creating cropped image.")
             return
 
-        embedding = self.embeddings.embed_face("unknown", webp.tobytes(), upsert=False)
+        embedding = self.embeddings.embed_face("nick", webp.tobytes(), upsert=True)
-        query_embedding = serialize(embedding)
+        res = self.face_classifier.classify_face(embedding)
-        best_faces = self._search_face(query_embedding)
-        logger.debug(f"Detected best faces for person as: {best_faces}")
 
-        if not best_faces or len(best_faces) < REQUIRED_FACES:
+        if not res:
-            logger.debug(f"{len(best_faces)} < {REQUIRED_FACES} min required faces.")
             return
 
-        sub_label = str(best_faces[0][0]).split("-")[0]
+        sub_label, score = res
-        avg_score = 0
 
-        for face in best_faces:
+        logger.debug(
-            score = 1.0 - face[1]
+            f"Detected best face for person as: {sub_label} with score {score}"
+        )
 
-            if face[0].split("-")[0] != sub_label:
+        if score < self.config.face_recognition.threshold or (
-                logger.debug("Detected multiple faces, result is not valid.")
+            id in self.detected_faces and score <= self.detected_faces[id]
-                return
-
-            avg_score += score
-
-        avg_score = round(avg_score / REQUIRED_FACES, 2)
-
-        if avg_score < self.config.face_recognition.threshold or (
-            id in self.detected_faces and avg_score <= self.detected_faces[id]
         ):
             logger.debug(
-                f"Recognized face score {avg_score} is less than threshold ({self.config.face_recognition.threshold}) / previous face score ({self.detected_faces.get(id)})."
+                f"Recognized face score {score} is less than threshold ({self.config.face_recognition.threshold}) / previous face score ({self.detected_faces.get(id)})."
             )
             return
 
@@ -482,12 +480,12 @@ class EmbeddingMaintainer(threading.Thread):
             json={
                 "camera": obj_data.get("camera"),
                 "subLabel": sub_label,
-                "subLabelScore": avg_score,
+                "subLabelScore": score,
             },
         )
 
         if resp.status_code == 200:
-            self.detected_faces[id] = avg_score
+            self.detected_faces[id] = score
 
     def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]:
         """Return the dimensions of the input image as [x, y, width, height]."""

frigate/util/model.py

@@ -2,9 +2,15 @@
 
 import logging
 import os
-from typing import Any
+from typing import Any, Optional
 
+import numpy as np
 import onnxruntime as ort
+from playhouse.sqliteq import SqliteQueueDatabase
+from sklearn.preprocessing import LabelEncoder, Normalizer
+from sklearn.svm import SVC
+
+from frigate.util.builtin import deserialize
 
 try:
     import openvino as ov
@@ -148,3 +154,41 @@ class ONNXModelRunner:
             return [infer_request.get_output_tensor().data]
         elif self.type == "ort":
             return self.ort.run(None, input)
+
+
+class FaceClassificationModel:
+    def __init__(self, db: SqliteQueueDatabase):
+        self.db = db
+        self.labeler: Optional[LabelEncoder] = None
+        self.classifier: Optional[SVC] = None
+
+    def __build_classifier(self) -> None:
+        faces: list[tuple[str, bytes]] = self.db.execute_sql(
+            "SELECT id, face_embedding FROM vec_faces"
+        ).fetchall()
+        embeddings = np.array([deserialize(f[1]) for f in faces])
+        self.labeler = LabelEncoder()
+        norms = Normalizer(norm="l2").transform(embeddings)
+        labels = self.labeler.fit_transform([f[0].split("-")[0] for f in faces])
+        self.classifier = SVC(kernel="linear", probability=True)
+        self.classifier.fit(norms, labels)
+
+    def clear_classifier(self) -> None:
+        self.classifier = None
+        self.labeler = None
+
+    def classify_face(self, embedding: np.ndarray) -> Optional[tuple[str, float]]:
+        if not self.classifier:
+            self.__build_classifier()
+
+        res = self.classifier.predict([embedding])
+
+        if not res:
+            return None
+
+        label = res[0]
+        probabilities = self.classifier.predict_proba([embedding])[0]
+        return (
+            self.labeler.inverse_transform([label])[0],
+            round(probabilities[label], 2),
+        )