[Toward Diverse Liveness Feature Representation and Domain Expansion for Cross-Domain Face Anti-Spoofing]
numpy==1.24.2
scikit_learn==1.2.2
torch==1.12.0
torchvision==0.14.1
Step 1: run python train.py
to train the DFANet model.
Step 2: run python test.py
to test the DFANet model.
If you use the DFANet, please cite the paper:
@inproceedings{huang2022towards,
title={Towards Diverse Liveness Feature Representation and Domain Expansion for Cross-Domain Face Anti-Spoofing},
author={Huang, Pei-Kai and Chong, Jun-Xiong and Ni, Hui-Yu and Chen, Tzu-Hsien and Hsu, Chiou-Ting},
booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)},
year={2023},
organization={IEEE}
}
We are students from MPLAB at National Tsing Hua University.
Chen, Tzu-Hsien [email protected]
Huang, Pei-Kai [email protected]
Ni, Hui-Yu [email protected]
Chong, Jun-Xiong [email protected]