(face_image, known_face_locations=None, num_jitters=1, model="small")
return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
Given an image, return the 128-dimension face encoding for each face in the image.

    :param face_image: The image that contains one or more faces
    :param known_face_locations: Optional - the bounding boxes of each face if you already know them.
    :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
    :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster.
    :return: A list of 128-dimensional face encodings (one for each face in the image)
Given an image, return the 128-dimension face encoding for each face in the image.
Given an image return the 128 - dimension face encoding for each face in the image .
def face_encodings(face_image, known_face_locations=None, num_jitters=1, model="small"):
    """
    Given an image, return the 128-dimension face encoding for each face in the image.

    :param face_image: The image that contains one or more faces
    :param known_face_locations: Optional - the bounding boxes of each face if you already know them.
    :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
    :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster.
    :return: A list of 128-dimensional face encodings (one for each face in the image)
    """
    raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model)
    return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
def face_encodings ( face_image , known_face_locations = None , num_jitters = 1 , model = "small" ) : raw_landmarks = _raw_face_landmarks ( face_image , known_face_locations , model ) return [ np . array ( face_encoder . compute_face_descriptor ( face_image , raw_landmark_set , num_jitters ) ) for raw_landmark_set in raw_landmarks ]