when i run my code it recognizes every one as Kyle Horton, why is that?

_______________Code Bellow_____________________________

import face_recognition import cv2

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the # other example, but it includes some basic performance tweaks to make things run a lot faster: # 1. Process each video frame at 1/4 resolution (though still display it at full resolution) # 2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one) video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it. kyle_image = face_recognition.load_image_file("kyle.jpg") kyle_face_encoding = face_recognition.face_encodings(kyle_image)[0]

# Load a second sample picture and learn how to recognize it. robert_image = face_recognition.load_image_file("robert.jpg") robert_face_encoding = face_recognition.face_encodings(robert_image)[0]

# Create arrays of known face encodings and their names known_face_encodings = [ kyle_face_encoding, robert_face_encoding ] known_face_names = [ "Kyle Horton", "Robert Horton" ]

# Initialize some variables face_locations = [] face_encodings = [] face_names = [] process_this_frame = True

while True: # Grab a single frame of video ret, frame = video_capture.read()

# Resize frame of video to 1/4 size for faster face recognition processing small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) rgb_small_frame = small_frame[:, :, ::-1]

# Only process every other frame of video to save time if process_this_frame: # Find all the faces and face encodings in the current frame of video face_locations = face_recognition.face_locations(rgb_small_frame) face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

face_names = [] for face_encoding in face_encodings: # See if the face is a match for the known face(s) matches = face_recognition.compare_faces(known_face_encodings, face_encoding) name = "Unknown"

# If a match was found in known_face_encodings, just use the first one. if True in matches: first_match_index = matches.index(True) name = known_face_names[first_match_index]

face_names.append(name)

process_this_frame = not process_this_frame

# Display the results for (top, right, bottom, left), name in zip(face_locations, face_names): # Scale back up face locations since the frame we detected in was scaled to 1/4 size top *= 4 right *= 4 bottom *= 4 left *= 4

# Draw a box around the face cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

# Draw a label with a name below the face cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

# Display the resulting image cv2.imshow('Video', frame)

# Hit 'q' on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord('q'): break

# Release handle to the webcam video_capture.release() cv2.destroyAllWindows()