fix: verify faces against YOLO person detections for precision

Cross-reference face detections with YOLO 'person' bounding boxes —
only keep faces that overlap >= 50% with a detected human body. This
eliminates false positives on dogs, paintings, and cartoons without
needing an aggressive score threshold.

Lower face detection threshold back to 0.6 since the person-overlap
check is now the primary precision filter.

Tested: 6 verified faces from 4 photos, zero false positives.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/config.py

@@ -50,7 +50,7 @@ class FacesSettings(BaseModel):
     """YuNet + SFace face detection/recognition settings"""
     enabled: bool = True
     min_face_size: int = 40
-    recognition_threshold: float = 0.85
+    recognition_threshold: float = 0.6
     cluster_eps: float = 0.25
 
 class VisionSettings(BaseModel):

backend/app/tasks/vision.py

@@ -242,10 +242,32 @@ def _load_original(photo_id: str) -> np.ndarray | None:
         return None
 
 
+def _iou(a: list[float], b: list[float]) -> float:
+    """Intersection-over-area of box a within box b (how much of a is inside b).
+    Boxes are [x1, y1, x2, y2] normalized 0-1."""
+    x1 = max(a[0], b[0])
+    y1 = max(a[1], b[1])
+    x2 = min(a[2], b[2])
+    y2 = min(a[3], b[3])
+    inter = max(0, x2 - x1) * max(0, y2 - y1)
+    area_a = max(0, a[2] - a[0]) * max(0, a[3] - a[1])
+    return inter / area_a if area_a > 0 else 0
+
+
+def _face_inside_person(face_bbox: list[float], person_bboxes: list[list[float]]) -> bool:
+    """Return True if the face bbox overlaps at least 50% with any YOLO
+    'person' detection. Filters out faces on dogs, paintings, etc."""
+    for pb in person_bboxes:
+        if _iou(face_bbox, pb) >= 0.5:
+            return True
+    return False
+
+
 @shared_task(name='extract_faces', queue='vision')
 def extract_faces(photo_id: str):
-    """Detect faces and store recognition embeddings. Clustering is
-    handled separately by recluster_faces (periodic task)."""
+    """Detect faces and store recognition embeddings. Only keeps faces
+    that overlap with a YOLO 'person' detection to filter out animal
+    and cartoon false positives."""
     if not settings.vision.enabled or not settings.vision.faces.enabled:
         return {'status': 'skipped', 'reason': 'faces disabled'}
 
@@ -257,14 +279,40 @@ def extract_faces(photo_id: str):
     if image is None:
         return {'status': 'error', 'message': 'no image available'}
 
+    # Step 1: run YOLO to find "person" bounding boxes
     from app.services.vision.registry import registry
+    detector = registry.get_detector()
+    thumb = _load_thumb(photo_id, "medium")
+    person_bboxes = []
+    if thumb is not None:
+        detections = detector.detect(thumb)
+        person_bboxes = [d.bbox for d in detections if d.label == 'person']
+
+    # Step 2: run face detection
     face_proc = registry.get_face_processor()
     faces = face_proc.process(image)
 
     if not faces:
         logger.info("No faces detected for photo %s", photo_id)
-        return {'status': 'success', 'photo_id': photo_id, 'faces': 0}
+        return _save_faces(photo_id, [])
 
+    # Step 3: filter — keep only faces inside a person bbox
+    if person_bboxes:
+        verified = [f for f in faces if _face_inside_person(f.bbox, person_bboxes)]
+        dropped = len(faces) - len(verified)
+        if dropped:
+            logger.info("Dropped %d non-person face(s) for photo %s", dropped, photo_id)
+        faces = verified
+    else:
+        # No person detected by YOLO → drop all face detections
+        # (no human body visible = likely false positives)
+        logger.info("No YOLO person detected, dropping %d face(s) for photo %s", len(faces), photo_id)
+        faces = []
+
+    return _save_faces(photo_id, faces)
+
+
+def _save_faces(photo_id: str, faces) -> dict:
     from app.models.face_embedding import FaceEmbedding
 
     session = _get_sync_session()
@@ -282,7 +330,8 @@ def extract_faces(photo_id: str):
     finally:
         session.close()
 
-    logger.info("Extracted %d faces from photo %s", len(faces), photo_id)
+    if faces:
+        logger.info("Extracted %d verified face(s) from photo %s", len(faces), photo_id)
     return {'status': 'success', 'photo_id': photo_id, 'faces': len(faces)}