wgqtmac / DR-UDA

This is the source code for paper "Unsupervised Adversarial Domain Adaptation for Cross-domain Face Presentation Attack Detection"

DR-UDA

This is the source code for paper ”Unsupervised Adversarial Domain Adaptation for Cross-domain Face Presentation Attack Detection“

Environment

This code is based on Python2.7, Pytorch 0.4.0, torchvision 0.2, CUDA 8.0

Data

Download the OULU-NPU, CASIA-FASD, Idiap Replay-Attack, MSU-MFSD and Rose-Youtu datasets

Data Processing

SeetaFace algotithm is utilized for face detection and face alignment. All the detected faces are normlaize to 256 x 256 x 3, where only RGB channels are utilized for training.

Training

python main.py

License

This project is released under the Apache 2.0 license.

If you find this work useful, please cite our papers with the following bibtex:

  @article{wang2020unsupervised,
  title={Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection},
  author={Wang, Guoqing and Han, Hu and Shan, Shiguang and Chen, Xilin},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={16},
  pages={56--69},
  year={2020},
  publisher={IEEE}
}

@inproceedings{guoqing19ada,
  title={Improving Cross-database Face Presentation Attack Detection via Adversarial Domain Adaptation},
  author={Guoqing Wang and Hu Han and Shiguang Shan and Xilin Chen},
  booktitle={Proc. ICB},
  year={2019}
}