This project is created by Tu Xiaoguang ([email protected]). Any questions pls open issues for my project, I will reply quickly.

To facility the research in the community of face anti-spoofing, we release our test codes (including the model) for GFA-CNN, the training code will be available upon the acceptance of our paper.

Usage: Tensorflow-1.12.0, python-2.7 // Download the "fda_model.rar" package at： https://pan.baidu.com/s/1P3_BrVkc0A4Y9wtL72PiAg using the password: "39lk". Or download the package here: https://drive.google.com/file/d/1UwAcCmmFBeik8Gjkl4mnAKRWTRcY1BPW/view?usp=sharing Put the package under the folder "FDA_codes/scripts", extract it and then type the below commonds in the terminal (you may need to modify the path in the script "evaluate_inFolder_mfsdPhoto.py" and also the path in the txt test-label files):

python evaluate_inFolder_mfsdPhoto.py --allow-different-dimensions --checkpoint fda_model/mfsdPhotoAtck/fns.ckpt --in-path content --out-path outputs/

Some results of the FDA:

Usage: Tensorflow-1.12.0, python-2.7 // Download the "models.rar" package at： https://pan.baidu.com/s/1Y3VlXPhGUCSRhRSEmRYIWg using the password: "bafq". Or download the package here: https://drive.google.com/file/d/1UWRCoM_Doq9bmh4bmrCf8U0SGfLnZ1GM/view?usp=sharing Put the package under the folder "models", extract it and then run the script: " interTest_anti_photo_merge_mfsd.py". You may need to change the path in this script and also the path in the txt test-label files.

Thanks for the authors in the fast-style-transfer for making their excellent works publicly available.

If you find our code is useful for your research, pls cite our work:

@article{tu2019learning,
  title={Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing},
  author={Tu, Xiaoguang and Zhao, Jian and Xie, Mei and Du, Guodong and Zhang, Hengsheng and Li, Jianshu and Ma, Zheng and Feng, Jiashi},
  journal={arXiv preprint arXiv:1901.05602},
  year={2019}
}