diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index 5ae05aa..2a6c94c 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -256,29 +256,6 @@ def analyze(
             - 'middle eastern': Confidence score for Middle Eastern ethnicity.
             - 'white': Confidence score for White ethnicity.
     """
-
-    if isinstance(img_path, np.ndarray) and len(img_path.shape) == 4:
-        # Received 4-D array, which means image batch.
-        # Check batch dimension and process each image separately.
-        if img_path.shape[0] > 1:
-            batch_resp_obj = []
-            # Execute analysis for each image in the batch.
-            for single_img in img_path:
-                resp_obj = demography.analyze(
-                    img_path=single_img,
-                    actions=actions,
-                    enforce_detection=enforce_detection,
-                    detector_backend=detector_backend,
-                    align=align,
-                    expand_percentage=expand_percentage,
-                    silent=silent,
-                    anti_spoofing=anti_spoofing,
-                )
-
-                # Append the response object to the batch response list.
-                batch_resp_obj.append(resp_obj)
-            return batch_resp_obj
-
     return demography.analyze(
         img_path=img_path,
         actions=actions,
diff --git a/deepface/modules/demography.py b/deepface/modules/demography.py
index 2258c1e..85cbe81 100644
--- a/deepface/modules/demography.py
+++ b/deepface/modules/demography.py
@@ -100,6 +100,30 @@ def analyze(
                - 'white': Confidence score for White ethnicity.
     """
 
+    if isinstance(img_path, np.ndarray) and len(img_path.shape) == 4:
+        # Received 4-D array, which means image batch.
+        # Check batch dimension and process each image separately.
+        if img_path.shape[0] > 1:
+            batch_resp_obj = []
+            # Execute analysis for each image in the batch.
+            for single_img in img_path:
+                # Call the analyze function for each image in the batch.
+                resp_obj = analyze(
+                    img_path=single_img,
+                    actions=actions,
+                    enforce_detection=enforce_detection,
+                    detector_backend=detector_backend,
+                    align=align,
+                    expand_percentage=expand_percentage,
+                    silent=silent,
+                    anti_spoofing=anti_spoofing,
+                )
+
+                # Append the response object to the batch response list.
+                batch_resp_obj.append(resp_obj)
+            return batch_resp_obj
+
+
     # if actions is passed as tuple with single item, interestingly it becomes str here
     if isinstance(actions, str):
         actions = (actions,)