This is a TensorFlow implementation of the face recognizer described in the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering". The project also uses ideas from the paper "Deep Face Recognition" from the Visual Geometry Group at Oxford.
The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python 3.5. The test cases can be found here and the results can be found here.
| Date | Update |
|---|---|
| 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see wiki). |
| 2018-03-31 | Added a new, more flexible input pipeline as well as a bunch of minor updates. |
| 2017-05-13 | Removed a bunch of older non-slim models. Moved the last bottleneck layer into the respective models. Corrected normalization of Center Loss. |
| 2017-05-06 | Added code to train a classifier on your own images. Renamed facenet_train.py to train_tripletloss.py and facenet_train_classifier.py to train_softmax.py. |
| 2017-03-02 | Added pretrained models that generate 128-dimensional embeddings. |
| 2017-02-22 | Updated to Tensorflow r1.0. Added Continuous Integration using Travis-CI. |
| 2017-02-03 | Added models where only trainable variables has been stored in the checkpoint. These are therefore significantly smaller. |
| 2017-01-27 | Added a model trained on a subset of the MS-Celeb-1M dataset. The LFW accuracy of this model is around 0.994. |
| 2017‑01‑02 | Updated to run with Tensorflow r0.12. Not sure if it runs with older versions of Tensorflow though. |