Misc fixes (#19089)

* Correctly handle min_faces

* Change to use recognized faces

docs/docs/configuration/face_recognition.md

@@ -67,7 +67,7 @@ Fine-tune face recognition with these optional parameters at the global level of
   - Default: `0.8`.
 - `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
   - Default: `0.9`.
-- `min_faces`: Min face attempts for the sub label to be applied to the person object.
+- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
   - Default: `1`
 - `save_attempts`: Number of images of recognized faces to save for training.
   - Default: `100`.

docs/docs/configuration/reference.md

@@ -561,7 +561,7 @@ face_recognition:
   recognition_threshold: 0.9
   # Optional: Min area of detected face box to consider running face recognition (default: shown below)
   min_area: 500
-  # Optional: Min face attempts for the sub label to be applied to the person object (default: shown below)
+  # Optional: Min face recognitions for the sub label to be applied to the person object (default: shown below)
   min_faces: 1
   # Optional: Number of images of recognized faces to save for training (default: shown below)
   save_attempts: 100

frigate/config/classification.py

@@ -84,7 +84,7 @@ class FaceRecognitionConfig(FrigateBaseModel):
         default=1,
         gt=0,
         le=6,
-        title="Min face attempts for the sub label to be applied to the person object.",
+        title="Min face recognitions for the sub label to be applied to the person object.",
     )
     save_attempts: int = Field(
         default=100, ge=0, title="Number of face attempts to save in the train tab."

frigate/data_processing/real_time/face.py

@@ -303,9 +303,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             self.person_face_history[id]
         )
 
-        if len(self.person_face_history[id]) < self.face_config.min_faces:
-            weighted_sub_label = "unknown"
-
         self.requestor.send_data(
             "tracked_object_update",
             json.dumps(
@@ -489,6 +486,10 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
         best_name = max(weighted_scores, key=weighted_scores.get)
 
+        # If the number of faces for this person < min_faces, we are not confident it is a correct result
+        if counts[best_name] < self.face_config.min_faces:
+            return None, 0.0
+
         # If the best name has the same number of results as another name, we are not confident it is a correct result
         for name, count in counts.items():
             if name != best_name and counts[best_name] == count: