SphereFace : Deep Hypersphere Embedding for Face Recognition

By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song

Introduction

The repository contains the entire pipeline (including all the preprossings) for deep face recognition with SphereFace. The recognition pipeline contains three major steps: face detection, face alignment and face recognition.

SphereFace is a recently proposed face recognition method. It was initially described in an arXiv technical report and then published in CVPR 2017. To facilitate the face recognition research, we give an example of training on CAISA-WebFace and testing on LFW.

The provided network prototxt example is a 28-layer CNN, which is the same as Center Face. To fully reproduce the results in the paper, you need to make some small modifications (network architecture) according to the SphereFace paper.

License

SphereFace is released under the MIT License (refer to the LICENSE file for details).

Citing SphereFace

If you find SphereFace useful in your research, please consider to cite:

@inproceedings{liu2017sphereface,
    author = {Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song},
    title = {SphereFace: Deep Hypersphere Embedding for Face Recognition},
    booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
    Year = {2017}
}

Contents

1. Update
2. Requirements
3. Installation
4. Usage

Update

• July 20, 2017
  • This repository was built.
• To be updated:
  • Our pretrained models, some intermediate results and extracted features will be released soon.

Requirements

1. Requirements for Matlab
2. Requirements for Caffe and matcaffe (see: Caffe installation instructions)
3. Requirements for MTCNN (see: MTCNN - face detection & alignment) and Pdollar toolbox (see: Piotr's Image & Video Matlab Toolbox).

Installation

1. Clone the SphereFace repository. We'll call the directory that you cloned SphereFace as SPHEREFACE_ROOT.

   git clone --recursive https://github.com/wy1iu/sphereface.git

2. Build Caffe and matcaffe

   cd $SPHEREFACE_ROOT/tools/caffe-sphereface
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make all -j8 && make matcaffe

Usage

After successfully completing installation, you'll be ready to run all the following experiments.

Part 1: Preprocessing

Note 1: In this part, we assume you are in the directory $SPHEREFACE_ROOT/preprocess/

1. Download the training set (CASIA-WebFace) and test set (LFW) and place them in $SPHEREFACE_ROOT/preprocess/data/.

   mv /your_path/CASIA_WebFace  data/
   ./code/get_lfw.sh
   tar xvf data/lfw.tgz -C data/

   Please make sure that the directory of data/ contains two datasets.

2. Detect faces and facial landmarks in CAISA-WebFace and LFW datasets using MTCNN (see: MTCNN - face detection & alignment).

   # In Matlab Command Window
   run code/face_detect_demo.m

   This will create a file dataList.mat in the directory of result/.

3. Align faces to a canonical pose using similarity transformation.

   # In Matlab Command Window
   run code/face_align_demo.m

   This will create two folders (CASIA-WebFace-112X96 and lfw-112X96) in the directory of result/, containing the aligned face images.

Part 2: Train

Note 2: In this part, we assume you are in the directory $SPHEREFACE_ROOT/train/

1. Get a list of training images and labels.

   mv ../preprocess/result/CASIA-WebFace-112X96 data/
   # In Matlab Command Window
   run code/get_list.m
   

   We move the aligned face images from preprocess folder to train folder and create a list CASIA-WebFace-112X96.txt in the directory of data/ for training.

2. Train sphereface model.

   ./code/sphereface/sphereface_train.sh 0,1

   We obtain a trained model sphereface_model_iter_28000.caffemodel and corresponding log file sphereface.log in the directory of result/sphereface/.

Part 3: Test

Note 3: In this part, we assume you are in the directory $SPHEREFACE_ROOT/test/

1. Get the pair list of LFW (view 2).

   mv ../preprocess/result/lfw-112X96 data/
   ./code/get_pairs.sh

   Make sure that the pairs.txt in the directory of data/

2. Extract deep features and test on LFW.

   # In Matlab Command Window
   run code/evaluation.m

   Finally we get the accuracy.

Contact

Yandong Wen and Weiyang Liu

Questions can also be left as issues in the repository. We will be happy to answer them.