diff --git a/face_recognition/Face_recognition_Updated.py b/face_recognition/Face_recognition_Updated.py
new file mode 100644
index 000000000..6c855a788
--- /dev/null
+++ b/face_recognition/Face_recognition_Updated.py
@@ -0,0 +1,44 @@
+import numpy as np
+import cv2
+
+# Load the image
+img = cv2.imread('test_image.jpg')
+
+# Convert the image to grayscale
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+# Load the pre-trained face detection classifier
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# Detect faces in the image
+faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+
+# Load the pre-trained face encoding model
+encoding_model = MyFaceEncodingModel()
+
+# Load the pre-stored face encodings and corresponding names
+encodings = np.load('face_encodings.npy')
+names = np.load('face_names.npy')
+
+# Encode all faces in the input image using the face encoding model
+encodings_img = []
+for (x, y, w, h) in faces:
+    face_gray = gray[y:y+h, x:x+w]
+    face_resized = cv2.resize(face_gray, encoding_model.input_shape[:2])
+    encoding = encoding_model.encode(face_resized)
+    encodings_img.append(encoding)
+encodings_img = np.array(encodings_img)
+
+# Compute the Euclidean distance between the encodings of all detected faces and all stored encodings
+distances = np.linalg.norm(encodings - encodings_img[:, np.newaxis], axis=-1)
+
+# Find the closest matching encoding for each detected face
+min_distance_indices = np.argmin(distances, axis=0)
+min_distances = distances[min_distance_indices, np.arange(len(min_distance_indices))]
+
+# Print the names corresponding to the closest matching encodings
+for i, distance in enumerate(min_distances):
+    if distance < 0.6:
+        print(names[min_distance_indices[i]])
+    else:
+        print("Unknown")