Our Generalizable Face AuthenticationCNN (GFA-CNN) is available at: https://arxiv.org/abs/1901.05602

This project is created by Tu Xiaoguang ([email protected])

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.

We add the codes for FDA in the folder "FDA_codes"

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". 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 the txt testing label files):

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

Citation

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} }