diff --git a/frigate/embeddings/maintainer.py b/frigate/embeddings/maintainer.py
index e7fa2c4f2..ca7d09238 100644
--- a/frigate/embeddings/maintainer.py
+++ b/frigate/embeddings/maintainer.py
@@ -103,7 +103,7 @@ class EmbeddingMaintainer(threading.Thread):
     def _process_requests(self) -> None:
         """Process embeddings requests"""
 
-        def _handle_request(topic: str, data: str) -> str:
+        def _handle_request(topic: str, data: dict[str, any]) -> str:
             try:
                 if topic == EmbeddingsRequestEnum.embed_description.value:
                     return serialize(
@@ -123,12 +123,34 @@ class EmbeddingMaintainer(threading.Thread):
                         self.embeddings.text_embedding([data])[0], pack=False
                     )
                 elif topic == EmbeddingsRequestEnum.register_face.value:
-                    self.embeddings.embed_face(
-                        data["face_name"],
-                        base64.b64decode(data["image"]),
-                        upsert=True,
-                    )
-                    return None
+                    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(
+                                base64.b64decode(data["image"]), dtype=np.uint8
+                            ),
+                            cv2.IMREAD_COLOR,
+                        )
+                        face_box = self._detect_face(img)
+
+                        if not face_box:
+                            return False
+
+                        face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
+                        ret, webp = cv2.imencode(
+                            ".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
+                        )
+                        self.embeddings.embed_face(
+                            data["face_name"], webp.tobytes(), upsert=True
+                        )
+
+                    return False
             except Exception as e:
                 logger.error(f"Unable to handle embeddings request {e}")
 
@@ -302,6 +324,29 @@ class EmbeddingMaintainer(threading.Thread):
         """
         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]))
+        faces = self.face_detector.detect(input)
+
+        if faces[1] is None:
+            return None
+
+        face = None
+
+        for _, potential_face in enumerate(faces[1]):
+            raw_bbox = potential_face[0:4].astype(np.uint16)
+            x: int = max(raw_bbox[0], 0)
+            y: int = max(raw_bbox[1], 0)
+            w: int = raw_bbox[2]
+            h: int = raw_bbox[3]
+            bbox = (x, y, x + w, y + h)
+
+            if face is None or area(bbox) > area(face):
+                face = bbox
+
+        return face
+
     def _process_face(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
         """Look for faces in image."""
         id = obj_data["id"]
@@ -331,27 +376,12 @@ 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)
 
-            self.face_detector.setInputSize((right - left, bottom - top))
-            faces = self.face_detector.detect(person)
-
-            if faces[1] is None:
+            if not face:
                 logger.debug("Detected no faces for person object.")
                 return
 
-            face = None
-
-            for _, potential_face in enumerate(faces[1]):
-                raw_bbox = potential_face[0:4].astype(np.int8)
-                x = max(raw_bbox[0], 0)
-                y = max(raw_bbox[1], 0)
-                w = raw_bbox[2]
-                h = raw_bbox[3]
-                bbox = (x, y, x + w, y + h)
-
-                if face is None or area(bbox) > area(face):
-                    face = bbox
-
             face_frame = person[face[1] : face[3], face[0] : face[2]]
             face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
         else:
@@ -384,7 +414,7 @@ class EmbeddingMaintainer(threading.Thread):
                 face_box[1] : face_box[3], face_box[0] : face_box[2]
             ]
 
-        ret, jpg = cv2.imencode(
+        ret, webp = cv2.imencode(
             ".webp", face_frame, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
         )
 
@@ -392,7 +422,7 @@ class EmbeddingMaintainer(threading.Thread):
             logger.debug("Not processing face due to error creating cropped image.")
             return
 
-        embedding = self.embeddings.embed_face("unknown", jpg.tobytes(), upsert=False)
+        embedding = self.embeddings.embed_face("unknown", webp.tobytes(), upsert=False)
         query_embedding = serialize(embedding)
         best_faces = self._search_face(query_embedding)
         logger.debug(f"Detected best faces for person as: {best_faces}")
@@ -409,7 +439,7 @@ class EmbeddingMaintainer(threading.Thread):
 
             if face[0].split("-")[0] != sub_label:
                 logger.debug("Detected multiple faces, result is not valid.")
-                return None
+                return
 
             avg_score += score
 
@@ -421,7 +451,7 @@ class EmbeddingMaintainer(threading.Thread):
             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)})."
             )
-            return None
+            return
 
         resp = requests.post(
             f"{FRIGATE_LOCALHOST}/api/events/{id}/sub_label",