diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index 7375cf40e..8fd58c168 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/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:
diff --git a/frigate/embeddings/maintainer.py b/frigate/embeddings/maintainer.py
index 2b3489a4c..feafcaa7d 100644
--- a/frigate/embeddings/maintainer.py
+++ b/frigate/embeddings/maintainer.py
@@ -406,7 +406,7 @@ class EmbeddingMaintainer(threading.Thread):
                 logger.debug("Detected no faces for person object.")
                 return
 
-            margin = int((face_box[2] - face_box[0]) * 0.4)
+            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
@@ -442,7 +442,7 @@ class EmbeddingMaintainer(threading.Thread):
                 return
 
             face_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
-            margin = int((face_box[2] - face_box[0]) * 0.4)
+            margin = int((face_box[2] - face_box[0]) * 0.25)
 
             face_frame = face_frame[
                 max(0, face_box[1] - margin) : min(