This is the official repository of "Deep Learning for Face Anti-Spoofing: A Survey", a comprehensive survey of recent progress in deep learning methods for face anti-spoofing (FAS) as well as the datasets and protocols.

If you find our work useful in your research, please consider citing:

@article{yu2022deep,
  title={Deep Learning for Face Anti-Spoofing: A Survey},
  author={Yu, Zitong and Qin, Yunxiao and Li, Xiaobai and Zhao, Chenxu and Lei, Zhen and Zhao, Guoying},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year={2022}
}

We present a comprehensive review of recent deep learning methods for face anti-spoofing (mostly from 2018 to 2022). It covers hybrid (handcrafted+deep), pure deep learning, and generalized learning based methods for monocular RGB face anti-spoofing. It also includes multi-modal learning based methods as well as specialized sensor based FAS. It also presents detailed comparision among publicly available datasets, together with several classical evaluation protocols.

🔔 We will update this page frequently~ 🎉🎉🎉

• Datasets
  • Using commercial RGB camera
  • With multiple modalities or specialized sensors
• Deep FAS methods with commercial RGB camera
  • Hybrid (handcrafted + deep)
  • End-to-end binary cross-entropy supervision
  • Pixel-wise auxiliary supervision
  • Generative model with pixel-wise supervision
  • Domain adaptation
  • Domain generalization
  • Zero/Few-shot learning
  • Anomaly detection
• Deep FAS methods with advanced sensor
  • Learning upon specialized sensor
  • Multi-modal learning

• temp