SphereFace : Deep Hypersphere Embedding for Face Recognition

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

License

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

Contents

0. Introduction
1. Citation
2. Requirements
3. Installation
4. Usage
5. Models
6. Results
7. Video Demo
8. Note
9. Third-party re-implementation

Introduction

The repository contains the entire pipeline (including all the preprocessings) 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 using the 20-layer CNN architecture described in the paper (i.e. SphereFace-20).

SphereFace achieves the state-of-the-art verification performance (previously No.1) in MegaFace Challenge under the small training set protocol.

Citation

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

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

Our another closely-related previous work in ICML'16 (more):

@inproceedings{liu2016large,
    author = {Liu, Weiyang and Wen, Yandong and Yu, Zhiding and Yang, Meng},
    title = {Large-Margin Softmax Loss for Convolutional Neural Networks},
    booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
    Year = {2016}
}

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
   make all -j8 && make matcaffe

Usage

After successfully completing the installation, you are ready to run all the following experiments.

Part 1: Preprocessing

Note: 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 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: 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
   

   The aligned face images in folder CASIA-WebFace-112X96/ are moved from preprocess folder to train folder. A list CASIA-WebFace-112X96.txt is created in the directory of data/ for the subsequent training.

2. Train the sphereface model.

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

   After training, a model sphereface_model_iter_28000.caffemodel and a corresponding log file sphereface_train.log are placed in the directory of result/sphereface/.

Part 3: Test

Note: 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 LFW dataset andpairs.txt in the directory of data/

2. Extract deep features and test on LFW.

   # In Matlab Command Window
   run code/evaluation.m

   Finally we have the sphereface_model.caffemodel, extracted features pairs.mat in folder result/, and accuracy on LFW like this:

   fold	1	2	3	4	5	6	7	8	9	10	AVE
   ACC	99.33%	99.17%	98.83%	99.50%	99.17%	99.83%	99.17%	98.83%	99.83%	99.33%	99.30%

Models

1. Visualizations of network architecture (tools from ethereon):
   • SphereFace-20: link
2. Model file
   • SphereFace-20: Google Drive | Baidu

Results

1. Following the instruction, we go through the entire pipeline for 5 times. The accuracies on LFW are shown below. Generally, we report the average but we release the model-3 here.

   Experiment	#1	#2	#3 (released)	#4	#5
   ACC	99.24%	99.20%	99.30%	99.27%	99.13%

2. Other intermediate results:

   • LFW features: Google Drive | Baidu
   • Training log: Google Drive | Baidu

Video Demo

Please click the image to watch the Youtube video. For Youku users, click here.

Details:

1. It is an open-set face recognition scenario. The video is processed frame by frame, following the same pipeline in this repository.
2. Gallery set consists of 6 identities. Each main character has only 1 gallery face image. All the detected faces are included in probe set.
3. There is no overlap between gallery set and training set (CASIA-WebFace).
4. The scores between each probe face and gallery set are computed by cosine similarity. If the maximal score of a probe face is smaller than a pre-definded threshold, the probe face would be considered as an outlier.
5. Main characters are labeled by boxes with different colors. ( Rachel, Monica, Phoebe, Joey, Chandler, Ross)

Note

1. Backward gradient

   • In this implementation, we did not strictly follow the equations in paper. Instead, we normalize the scale of gradient. It can be interpreted as a varying strategy for learning rate to help converge more stably. Similar idea and intuition also appear in normalized gradients and projected gradient descent.
   • More specifically, if the original gradient of f w.r.t x can be written as df/dx = coeff_w * w + coeff_x * x, we use the normalized version [df/dx] = (coeff_w * w + coeff_x * x) / norm_wx to perform backward propragation, where norm_wx is sqrt(coeff_w^2 + coeff_x^2). The same operation is also applied to the gradient of f w.r.t w.
   • In fact, you do not necessarily need to use the original gradient, since the original gradient sometimes is not an optimal design. One important criterion for modifying the backprop gradient is that the new "gradient" (strictly speaking, it is not a gradient anymore) need to make the objective value decrease stably and consistently. (In terms of some failure cases for gradient-based back-prop, I recommand a great talk by Shai Shalev-Shwartz)
   • If you use the original gradient to do the backprop, you could still make it work but may need different lambda settings, iteration number and learning rate decay strategy.

2. Lambda and Note for training (When the loss becomes 87)

   • Please refer to our previous note and explanation.

Third-party re-implementation

• PyTorch: code by carwin.
• Caffe2: code by tpys.
• System: A cool face demo system using SphereFace by tpys.

Contact

Weiyang Liu and Yandong Wen

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