Instance-Aware Domain Generalization for Face Anti-Spoofing

This is the PyTorch implementation of our paper:

[Paper] Instance-Aware Domain Generalization for Face Anti-Spoofing

Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma.

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023

[Arxiv] [Paper]

Updates

• (October 2023) All checkpoints of pretrained models are released.
• (October 2023) All code of IADG are released.

Installation

Requirements

• Linux, CUDA>=11.7, GCC>=5.4

• Python>=3.10

  We recommend you to use Anaconda to create a conda environment:

  conda create -n IADG python=3.10 pip

  Then, activate the environment:

  conda activate IADG

• PyTorch>=1.13.0, torchvision>=0.14.0 (following instructions here

  For example, if your CUDA version is 11.7, you could install pytorch and torchvision as following:

  conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 -c pytorch

• Other requirements

  pip install -r requirements.txt

Usage

Checkpoints

Below, we provide all checkpoints, all training logs and inference logs of IADG for different datasets.

DownLoad Link of Google Drive

DownLoad Link of Baidu Netdisk (password:26xc)

Training

Training on single node

You can use the following training command.

CUDA_VISIBLE_DEVICES=0 python3 -u -m torch.distributed.launch --nproc_per_node=1 --master_port 17850 ./train.py -c ./configs/ICM2O.yaml

Evaluation

And then run following command to evaluate it on the testing set:

CUDA_VISIBLE_DEVICES=0 python3 -u  ./test.py -c ./configs/ICM2O_test.yaml --ckpt checkpoint_file

Acknowledgements

This project is based on the following open-source projects. We thank their authors for making the source code publically available.

• TFace

Citing IADG

If you find IADG useful in your research, please consider citing:

@inproceedings{zhou2023instance,
  title={Instance-Aware Domain Generalization for Face Anti-Spoofing},
  author={Zhou, Qianyu and Zhang, Ke-Yue and Yao, Taiping and Lu, Xuequan and Yi, Ran and Ding, Shouhong and Ma, Lizhuang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={20453--20463},
  year={2023}
}

License

This project is released under the Apache License 2.0, while some specific features in this repository are with other licenses. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.