dlib face landmark comparison example

68_point_face_landmark_detection_example.py

# load the required model for 68 points detection
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
face_rec = dlib.face_recognition_model_v1('dlib_face_recognition_resnet_model_v1.dat')

# iterate through all the detected faces
if (len(face_locations) > 0):
  for face_location in face_locations:
    # get 68 points landmark 
    face_encoding = predictor(frame, face_location)
    face_descriptor = face_rec.compute_face_descriptor(frame, face_encoding, 1)

    # See if the face is a match for the known face(s)
    match = list((np.linalg.norm(saved_face_descriptor - (np.array(face_descriptor)), axis=1)))
    val, idx = min((val, idx) for (idx, val) in enumerate(match))

    name = "Unknown"
    if (val < 0.6):
        name = names[idx]