pei-kaihuang / dfanet

ICME'2023 Toward Diverse Liveness Feature Representation and Domain Expansion for Cross-Domain Face Anti-Spoofing

DFANet

[Toward Diverse Liveness Feature Representation and Domain Expansion for Cross-Domain Face Anti-Spoofing]

Network Architecture

Requirements

numpy==1.24.2
scikit_learn==1.2.2
torch==1.12.0
torchvision==0.14.1

Training & Testing

Step 1: run python train.py to train the DFANet model.

Step 2: run python test.py to test the DFANet model.

Visualization:

Citation

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}
}

Contact us

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]