Face recognition framework based on PyTorch.

Introduction

This is a face recognition framework based on PyTorch with convenient training, evaluation and feature extraction functions. It is originally a multi-task face recognition framework for our accpeted ECCV 2018 paper, "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition". However, it is also a common framework for face recognition. You can freely customize your experiments with your data and configurations with it.

Paper

Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy, "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition", ECCV 2018

Project Page: link

Why multi-task?

Different datasets have different identity (category) sets. We do not know the intersection between them. Hence instead of merging identity sets of different datasets, regarding them as different tasks is an effective alternative way.

Features

Framework: Multi-task, Single Task

Loss: Softmax Loss, ArcFace

Backbone CNN: ResNet, DenseNet, Inception, InceptionResNet, NASNet, VGG

Benchmarks: Megaface (FaceScrub), IJB-A, LFW, CFP-FF, CFP-FP, AgeDB-30, calfw, cplfw

Data aug: flip, scale, translation

Online testing and visualization with Tensorboard.

Setup step by step

Clone the project.

git clone git@github.com:XiaohangZhan/face_recognition_framework.git
cd face_recognition_framework

Dependency.

python>=3.6, tensorboardX, pytorch>=0.3.1, mxnet, sklearn

Data Preparation.

Download datasets from insightface into your data storage folder, e.g., ~/data/face_recognition/. Taking CASIA-Webface for example:

cd ~/data/face_recognition/
unzip faces_CASIA_112x112.zip
cd - # back to the repo root
python tools/convert_data.py -r ~/data/face_recognition/faces_webface_112x112 -o ~/data/face_recognition/faces_webface_112x112 # convert mxnet records into images
ln -s ~/data/face_recognition/faces_webface_112x112 data/webface

Optionally, if you want to test on MegaFace. Download testing set from here into your data storage folder, e.g., ~/data/face_recognition/. Then:

cd ~/data/face_recognition/
mkdir -p megaface_test/raw
cd megaface_test/raw
mv ../../megaface_testpack_v1.0.zip .
unzip -q megaface_testpack_v1.0.zip
cd $THIS_REPO # back to the repo root
ln -s  ~/data/face_recognition/megaface_test data/megaface_test

Next, download MegaFace lists from here into ~/data/face_recognition/megaface_test/. Finally, the folder data/megaface_test/ looks like:

data
  ├── megaface_test
    ├── concat_list.txt
    ├── facescrub3530
    ├── megaface_distractor
    ├── raw

Training.

sh experiments/webface/res50-bs64-sz224-ep35/train.sh

Monitoring.
```
tensorboard --logdir experiments
```

Resume training.

sh experiments/webface/res50-bs64-sz224-ep35/resume.sh 10 # e.g., resume from epoch 10

Evalution.

sh experiments/webface/res50-bs64-sz224-ep35/evaluation.sh 35 # e.g., evaluate epoch 35

Feature extraction.

Firstly, specify the data_name, data_root and data_list under extract_info in the config file. The data_list is a txt file containing an image relative filename in each line. Then execute:
```
# e.g., extract features with epoch 35 model.
# The feature file is stored in checkpoints/ckpt_epoch_35_[data_name].bin
sh experiments/webface/res50-bs64-sz224-ep35/extract.sh 35 
```

Baselines

Trained using Webface

arch	LFW	CFP-FF	CFP-FP	AgeDB-30	calfw	cplfw
resnet-50	0.9850	0.9804	0.9117	0.8967	0.9013	0.8423

Trained using MS1M

arch	LFW	CFP-FF	CFP-FP	AgeDB-30	calfw	cplfw	vgg2-FP	megaface
densenet-121	0.9948	0.9946	0.9594	0.9615	0.9500	0.9057	0.9418	0.8665
densenet-121-arc	0.9973	0.9979	0.9601	0.9728	0.9558	0.9063	0.9496	0.9287

Note that the hyper-parameters are not adjusted to optimal. Hence, they are not the state-of-the-art face recognition models. You may download those pre-trained models here.

Bibtex

If you find this code useful in your research, please cite:

@inproceedings{zhan2018consensus,
  title={Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition},
  author={Zhan, Xiaohang and Liu, Ziwei and Yan, Junjie and Lin, Dahua and Change Loy, Chen},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={568--583},
  year={2018}
}

TODO (Will carry out in a "Buddha-like" way)

Implement distributed training.
Adjust hyper-parameters.
Multi-task experiments.

DonYum/face_recognition_framework