Yesnew

README.md

@@ -410,6 +410,3 @@ If you run into problems, please read the [Common Errors](https://github.com/age
   his [blog post](http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html).
 * Thanks to everyone who works on all the awesome Python data science libraries like numpy, scipy, scikit-image,
   pillow, etc, etc that makes this kind of stuff so easy and fun in Python.
-* Thanks to [Cookiecutter](https://github.com/audreyr/cookiecutter) and the
-  [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template
-  for making Python project packaging way more tolerable.

face_recognition/api.py

@@ -214,13 +214,3 @@ def face_encodings(face_image, known_face_locations=None, num_jitters=1, model="
     return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
 
 
-def compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6):
-    """
-    Compare a list of face encodings against a candidate encoding to see if they match.
-
-    :param known_face_encodings: A list of known face encodings
-    :param face_encoding_to_check: A single face encoding to compare against the list
-    :param tolerance: How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance.
-    :return: A list of True/False values indicating which known_face_encodings match the face encoding to check
-    """
-    return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)