diff --git a/frigate/api/classification.py b/frigate/api/classification.py
index d3ee9c3d9..975a41c9d 100644
--- a/frigate/api/classification.py
+++ b/frigate/api/classification.py
@@ -198,6 +198,16 @@ async def register_face(request: Request, name: str, file: UploadFile):
 
     context: EmbeddingsContext = request.app.embeddings
     result = context.register_face(name, await file.read())
+
+    if not isinstance(result, dict):
+        return JSONResponse(
+            status_code=500,
+            content={
+                "success": False,
+                "message": "Could not process request. Try restarting Frigate.",
+            },
+        )
+
     return JSONResponse(
         status_code=200 if result.get("success", True) else 400,
         content=result,
@@ -214,6 +224,16 @@ async def recognize_face(request: Request, file: UploadFile):
 
     context: EmbeddingsContext = request.app.embeddings
     result = context.recognize_face(await file.read())
+
+    if not isinstance(result, dict):
+        return JSONResponse(
+            status_code=500,
+            content={
+                "success": False,
+                "message": "Could not process request. Try restarting Frigate.",
+            },
+        )
+
     return JSONResponse(
         status_code=200 if result.get("success", True) else 400,
         content=result,
diff --git a/frigate/data_processing/common/face/model.py b/frigate/data_processing/common/face/model.py
index 5e15a2441..f7ef1ae13 100644
--- a/frigate/data_processing/common/face/model.py
+++ b/frigate/data_processing/common/face/model.py
@@ -1,5 +1,7 @@
 import logging
 import os
+import queue
+import threading
 from abc import ABC, abstractmethod
 
 import cv2
@@ -18,10 +20,7 @@ class FaceRecognizer(ABC):
 
     def __init__(self, config: FrigateConfig) -> None:
         self.config = config
-        self.landmark_detector = cv2.face.createFacemarkLBF()
-        self.landmark_detector.loadModel(
-            os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
-        )
+        self.init_landmark_detector()
 
     @abstractmethod
     def build(self) -> None:
@@ -37,6 +36,13 @@ class FaceRecognizer(ABC):
     def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         pass
 
+    def init_landmark_detector(self) -> None:
+        landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
+
+        if os.path.exists(landmark_model):
+            self.landmark_detector = cv2.face.createFacemarkLBF()
+            self.landmark_detector.loadModel(landmark_model)
+
     def align_face(
         self,
         image: np.ndarray,
@@ -130,6 +136,7 @@ class LBPHRecognizer(FaceRecognizer):
 
     def build(self):
         if not self.landmark_detector:
+            self.init_landmark_detector()
             return None
 
         labels = []
@@ -201,45 +208,69 @@ class ArcFaceRecognizer(FaceRecognizer):
         super().__init__(config)
         self.mean_embs: dict[int, np.ndarray] = {}
         self.face_embedder: ArcfaceEmbedding = ArcfaceEmbedding()
+        self.model_builder_queue: queue.Queue | None = None
 
     def clear(self) -> None:
         self.mean_embs = {}
 
-    def build(self):
-        if not self.landmark_detector:
-            return None
+    def run_build_task(self) -> None:
+        self.model_builder_queue = queue.Queue()
 
-        face_embeddings_map: dict[str, list[np.ndarray]] = {}
-        idx = 0
+        def build_model():
+            face_embeddings_map: dict[str, list[np.ndarray]] = {}
+            idx = 0
 
-        dir = "/media/frigate/clips/faces"
-        for name in os.listdir(dir):
-            if name == "train":
-                continue
-
-            face_folder = os.path.join(dir, name)
-
-            if not os.path.isdir(face_folder):
-                continue
-
-            face_embeddings_map[name] = []
-            for image in os.listdir(face_folder):
-                img = cv2.imread(os.path.join(face_folder, image))
-
-                if img is None:
+            dir = "/media/frigate/clips/faces"
+            for name in os.listdir(dir):
+                if name == "train":
                     continue
 
-                img = self.align_face(img, img.shape[1], img.shape[0])
-                emb = self.face_embedder([img])[0].squeeze()
-                face_embeddings_map[name].append(emb)
+                face_folder = os.path.join(dir, name)
 
-            idx += 1
+                if not os.path.isdir(face_folder):
+                    continue
+
+                face_embeddings_map[name] = []
+                for image in os.listdir(face_folder):
+                    img = cv2.imread(os.path.join(face_folder, image))
+
+                    if img is None:
+                        continue
+
+                    img = self.align_face(img, img.shape[1], img.shape[0])
+                    emb = self.face_embedder([img])[0].squeeze()
+                    face_embeddings_map[name].append(emb)
+
+                idx += 1
+
+            self.model_builder_queue.put(face_embeddings_map)
+
+        thread = threading.Thread(target=build_model, daemon=True)
+        thread.start()
+
+    def build(self):
+        if not self.landmark_detector:
+            self.init_landmark_detector()
+            return None
+
+        if self.model_builder_queue is not None:
+            try:
+                face_embeddings_map: dict[str, list[np.ndarray]] = (
+                    self.model_builder_queue.get(timeout=0.1)
+                )
+                self.model_builder_queue = None
+            except queue.Empty:
+                return
+        else:
+            self.run_build_task()
+            return
 
         if not face_embeddings_map:
             return
 
         for name, embs in face_embeddings_map.items():
-            self.mean_embs[name] = stats.trim_mean(embs, 0.15)
+            if embs:
+                self.mean_embs[name] = stats.trim_mean(embs, 0.15)
 
         logger.debug("Finished building ArcFace model")