diff --git a/frigate/data_processing/common/face/model.py b/frigate/data_processing/common/face/model.py
index f7ef1ae13..1af934c5d 100644
--- a/frigate/data_processing/common/face/model.py
+++ b/frigate/data_processing/common/face/model.py
@@ -10,7 +10,7 @@ from scipy import stats
 
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
-from frigate.embeddings.onnx.facenet import ArcfaceEmbedding
+from frigate.embeddings.onnx.face_embedding import ArcfaceEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -329,7 +329,7 @@ class ArcFaceRecognizer(FaceRecognizer):
             cosine_similarity = dot_product / (magnitude_A * magnitude_B)
             confidence = self.similarity_to_confidence(cosine_similarity)
 
-            if cosine_similarity > score:
+            if confidence > score:
                 score = confidence
                 label = name
 
diff --git a/frigate/embeddings/onnx/facenet.py b/frigate/embeddings/onnx/face_embedding.py
similarity index 96%
rename from frigate/embeddings/onnx/facenet.py
rename to frigate/embeddings/onnx/face_embedding.py
index 3439620a0..0b808f716 100644
--- a/frigate/embeddings/onnx/facenet.py
+++ b/frigate/embeddings/onnx/face_embedding.py
@@ -90,8 +90,7 @@ class ArcfaceEmbedding(BaseEmbedding):
         frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
 
         # run arcface normalization
-        normalized_image = frame.astype(np.float32) / 255.0
-        frame = (normalized_image - 0.5) / 0.5
+        frame = (frame / 127.5) - 1.0
 
         frame = np.transpose(frame, (2, 0, 1))
         frame = np.expand_dims(frame, axis=0)