diff --git a/deepface/commons/distance.py b/deepface/commons/distance.py
index 6048b9e..950a7a2 100644
--- a/deepface/commons/distance.py
+++ b/deepface/commons/distance.py
@@ -2,7 +2,7 @@ from typing import Union
 import numpy as np
 
 
-def findCosineDistance(
+def find_cosine_distance(
     source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
 ) -> np.float64:
     if isinstance(source_representation, list):
@@ -17,7 +17,7 @@ def findCosineDistance(
     return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
 
 
-def findEuclideanDistance(
+def find_euclidean_distance(
     source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
 ) -> np.float64:
     if isinstance(source_representation, list):
@@ -38,7 +38,7 @@ def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
     return x / np.sqrt(np.sum(np.multiply(x, x)))
 
 
-def findThreshold(model_name: str, distance_metric: str) -> float:
+def find_threshold(model_name: str, distance_metric: str) -> float:
 
     base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}
 
diff --git a/deepface/modules/recognition.py b/deepface/modules/recognition.py
index f678a2b..6c21e35 100644
--- a/deepface/modules/recognition.py
+++ b/deepface/modules/recognition.py
@@ -249,11 +249,11 @@ def find(
                 )
 
             if distance_metric == "cosine":
-                distance = dst.findCosineDistance(source_representation, target_representation)
+                distance = dst.find_cosine_distance(source_representation, target_representation)
             elif distance_metric == "euclidean":
-                distance = dst.findEuclideanDistance(source_representation, target_representation)
+                distance = dst.find_euclidean_distance(source_representation, target_representation)
             elif distance_metric == "euclidean_l2":
-                distance = dst.findEuclideanDistance(
+                distance = dst.find_euclidean_distance(
                     dst.l2_normalize(source_representation),
                     dst.l2_normalize(target_representation),
                 )
@@ -263,7 +263,7 @@ def find(
             distances.append(distance)
 
             # ---------------------------
-        target_threshold = threshold or dst.findThreshold(model_name, distance_metric)
+        target_threshold = threshold or dst.find_threshold(model_name, distance_metric)
 
         result_df["threshold"] = target_threshold
         result_df["distance"] = distances
diff --git a/deepface/modules/verification.py b/deepface/modules/verification.py
index 9310e82..ab6c9dd 100644
--- a/deepface/modules/verification.py
+++ b/deepface/modules/verification.py
@@ -133,11 +133,11 @@ def verify(
             img2_representation = img2_embedding_obj[0]["embedding"]
 
             if distance_metric == "cosine":
-                distance = dst.findCosineDistance(img1_representation, img2_representation)
+                distance = dst.find_cosine_distance(img1_representation, img2_representation)
             elif distance_metric == "euclidean":
-                distance = dst.findEuclideanDistance(img1_representation, img2_representation)
+                distance = dst.find_euclidean_distance(img1_representation, img2_representation)
             elif distance_metric == "euclidean_l2":
-                distance = dst.findEuclideanDistance(
+                distance = dst.find_euclidean_distance(
                     dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation)
                 )
             else:
@@ -147,7 +147,7 @@ def verify(
             regions.append((img1_region, img2_region))
 
     # -------------------------------
-    threshold = dst.findThreshold(model_name, distance_metric)
+    threshold = dst.find_threshold(model_name, distance_metric)
     distance = min(distances)  # best distance
     facial_areas = regions[np.argmin(distances)]
 
diff --git a/tests/face-recognition-how.py b/tests/face-recognition-how.py
index 36ec45b..09ad8cf 100644
--- a/tests/face-recognition-how.py
+++ b/tests/face-recognition-how.py
@@ -38,7 +38,7 @@ distance_vector = np.square(img1_representation - img2_representation)
 current_distance = np.sqrt(distance_vector.sum())
 logger.info(f"Euclidean distance: {current_distance}")
 
-threshold = distance.findThreshold(model_name=model_name, distance_metric="euclidean")
+threshold = distance.find_threshold(model_name=model_name, distance_metric="euclidean")
 logger.info(f"Threshold for {model_name}-euclidean pair is {threshold}")
 
 if current_distance < threshold:
diff --git a/tests/test_find.py b/tests/test_find.py
index 8d9e34f..1d3faa7 100644
--- a/tests/test_find.py
+++ b/tests/test_find.py
@@ -6,7 +6,7 @@ from deepface.commons.logger import Logger
 
 logger = Logger("tests/test_find.py")
 
-threshold = distance.findThreshold(model_name="VGG-Face", distance_metric="cosine")
+threshold = distance.find_threshold(model_name="VGG-Face", distance_metric="cosine")
 
 
 def test_find_with_exact_path():