ArcFace face recognition

Implementation of the ArcFace face recognition algorithm. It includes a pre-trained model based on ResNet50.

The code is based on peteryuX's implementation. Instead of using full Tensorflow for the inference, the model has been converted to a Tensorflow lite model using tf.lite.TFLiteConverter which increased the speed of the inference by a factor of ~2.27.

Installation

You can install the package through pip:

pip install arcface

Quick start

The following example illustrates the ease of use of this package:

>>> from arcface import ArcFace
>>> face_rec = ArcFace.ArcFace()
>>> emb1 = face_rec.calc_emb("~/Downloads/test.jpg")
>>> print(emb1)
array([-1.70827676e-02, -2.69084200e-02, -5.85994311e-02,  3.33652040e-03,
        9.58345132e-04,  1.21807214e-02, -6.81217164e-02, -1.33364811e-03,
       -2.12905575e-02,  1.67165045e-02,  3.52908894e-02, -5.26051633e-02,
	   ...
       -2.11241804e-02,  2.22553015e-02, -5.71946353e-02, -2.33468022e-02],
      dtype=float32)
>>> emb2 = face_rec.calc_emb("~/Downloads/test2.jpg")
>>> face_rec.get_distance_embeddings(emb1, emb2)
0.78542

You can feed the calc_emb function either a single image or an array of images. Furthermore, you can supply the image as (absolute or relative) path, or an cv2-image. To make it more clear, hear are the four possibilities:

1. (Absolute or relative) path to a single image: face_rec.calc_emb("test.jpg")
2. Array of images: face_rec.calc_emb(["test1.jpg", "test2.png"])
3. Single cv2-image: face_rec.calc_emb(cv2.imread("test.png"))
4. Array of cv2-images: face_rec.calc_emb([cv2.imread("test1.jpg"), cv2.imread("test2.png")])

The face recognition tool returns (an array of) 512-d embedding(s) as a numpy array.

Notice! This package does neither perform face detection nor face alignment! It assumes that the images are already pre-processsed!

Benchmark

* executed on a CPU: Intel i7-10510U

License

Licensed under the EUPL, Version 1.2 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence"). You may not use this work except in compliance with the Licence.

License: European Union Public License v1.2