diff --git a/frigate/data_processing/common/face/model.py b/frigate/data_processing/common/face/model.py
index 56065d3ff..2fc98ebc1 100644
--- a/frigate/data_processing/common/face/model.py
+++ b/frigate/data_processing/common/face/model.py
@@ -108,21 +108,22 @@ class FaceRecognizer(ABC):
             image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
         )
 
-    def get_blur_confidence_reduction(self, input: np.ndarray) -> tuple[float, float]:
+    def get_blur_confidence_reduction(self, input: np.ndarray) -> float:
         """Calculates the reduction in confidence based on the blur of the image."""
         if not self.config.face_recognition.blur_confidence_filter:
-            return 0, 0.0
+            return 0.0
 
         variance = cv2.Laplacian(input, cv2.CV_64F).var()
+        logger.debug(f"face detected with blurriness {variance}")
 
-        if variance < 80:  # image is very blurry
-            return variance, 0.05
-        elif variance < 100:  # image moderately blurry
-            return variance, 0.03
-        elif variance < 150:  # image is slightly blurry
-            return variance, 0.01
+        if variance < 120:  # image is very blurry
+            return 0.06
+        elif variance < 160:  # image moderately blurry
+            return 0.04
+        elif variance < 200:  # image is slightly blurry
+            return 0.02
         else:
-            return variance, 0.0
+            return 0.0
 
 
 def similarity_to_confidence(
@@ -234,8 +235,7 @@ class FaceNetRecognizer(FaceRecognizer):
         # face recognition is best run on grayscale images
 
         # get blur factor before aligning face
-        variance, blur_reduction = self.get_blur_confidence_reduction(face_image)
-        logger.debug(f"face detected with blurriness {variance}")
+        blur_reduction = self.get_blur_confidence_reduction(face_image)
 
         # align face and run recognition
         img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
@@ -345,8 +345,7 @@ class ArcFaceRecognizer(FaceRecognizer):
         # face recognition is best run on grayscale images
 
         # get blur reduction before aligning face
-        variance, blur_reduction = self.get_blur_confidence_reduction(face_image)
-        logger.debug(f"face detected with blurriness {variance}")
+        blur_reduction = self.get_blur_confidence_reduction(face_image)
 
         # align face and run recognition
         img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])