(face_encodings, face_to_compare)
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
    for each comparison face. The distance tells you how similar the faces are.

    :param face_encodings: List of face encodings to compare
    :param face_to_compare: A face encoding to compare against
    :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
    for each comparison face. The distance tells you how similar the faces are.
Given a list of face encodings compare them to a known face encoding and get a euclidean distance for each comparison face . The distance tells you how similar the faces are .
def face_distance(face_encodings, face_to_compare):
    """
    Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
    for each comparison face. The distance tells you how similar the faces are.

    :param face_encodings: List of face encodings to compare
    :param face_to_compare: A face encoding to compare against
    :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
    """
    if len(face_encodings) == 0:
        return np.empty((0))

    return np.linalg.norm(face_encodings - face_to_compare, axis=1)
def face_distance ( face_encodings , face_to_compare ) : if len ( face_encodings ) == 0 : return np . empty ( ( 0 ) ) return np . linalg . norm ( face_encodings - face_to_compare , axis = 1 )