diff --git a/deepface/models/face_detection/CenterFace.py b/deepface/models/face_detection/CenterFace.py
index b8fdf6b..7e7d72e 100644
--- a/deepface/models/face_detection/CenterFace.py
+++ b/deepface/models/face_detection/CenterFace.py
@@ -1,6 +1,6 @@
 # built-in dependencies
 import os
-from typing import List
+from typing import List, Union
 
 # 3rd party dependencies
 import numpy as np
@@ -34,12 +34,29 @@ class CenterFaceClient(Detector):
 
         return CenterFace(weight_path=weights_path)
 
-    def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
+    def detect_faces(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]:
         """
         Detect and align face with CenterFace
 
         Args:
-            img (np.ndarray): pre-loaded image as numpy array
+            img (Union[np.ndarray, List[np.ndarray]]): pre-loaded image as numpy array or a list of those
+
+        Returns:
+            results (Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]): A list or a list of lists of FacialAreaRegion objects
+        """
+        if isinstance(img, np.ndarray):
+            return self._process_single_image(img)
+        elif isinstance(img, list):
+            return [self._process_single_image(single_img) for single_img in img]
+        else:
+            raise ValueError("Input must be a numpy array or a list of numpy arrays.")
+
+    def _process_single_image(self, single_img: np.ndarray) -> List[FacialAreaRegion]:
+        """
+        Helper function to detect faces in a single image.
+
+        Args:
+            single_img (np.ndarray): pre-loaded image as numpy array
 
         Returns:
             results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
@@ -53,7 +70,7 @@ class CenterFaceClient(Detector):
         #     img, img.shape[0], img.shape[1], threshold=threshold
         # )
         detections, landmarks = self.build_model().forward(
-            img, img.shape[0], img.shape[1], threshold=threshold
+            single_img, single_img.shape[0], single_img.shape[1], threshold=threshold
         )
 
         for i, detection in enumerate(detections):