Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Liu, Shubao; Zhang, Ke-Yue; Yao, Taiping; Sheng, Kekai; Ding, Shouhong; Tai, Ying; Li, Jilin; Xie, Yuan; Ma, Lizhuang

doi:10.24963/ijcai.2021/120

Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Shubao Liu, Ke-Yue Zhang, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Yuan Xie, Lizhuang Ma

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 867-873.

https://doi.org/10.24963/ijcai.2021/120

Keywords

Computer Vision: Biometrics, Face and Gesture Recognition

Abstract

Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. Concretely, Sample Reweighting Module is first proposed to identify samples with relatively large domain bias, and reduce their impact on the overall optimization. Afterwards, Feature Reweighting Module is introduced to focus on these samples and extract more domain-irrelevant features via a self-distilling mechanism. Combined with the domain discriminator, the iteration of the two modules promotes the extraction of generalized features. Extensive experiments and visualizations are presented to demonstrate the effectiveness and interpretability of our method against the state-of-the-art competitors.

Shubao Liu, Ke-Yue Zhang, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Yuan Xie, and Lizhuang Ma. 2021. Dual Reweighting Domain Generalization for Face Presentation Attack Detection. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 867–873. https://doi.org/10.24963/ijcai.2021/120

@inproceedings{ijcai2021p120,
  title     = {Dual Reweighting Domain Generalization for Face Presentation Attack Detection},
  author    = {Liu, Shubao and Zhang, Ke-Yue and Yao, Taiping and Sheng, Kekai and Ding, Shouhong and Tai, Ying and Li, Jilin and Xie, Yuan and Ma, Lizhuang},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {867--873},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/120},
  url       = {https://doi.org/10.24963/ijcai.2021/120},
}

TY  - CPAPER
TI  - Dual Reweighting Domain Generalization for Face Presentation Attack Detection
AU  - Liu, Shubao
AU  - Zhang, Ke-Yue
AU  - Yao, Taiping
AU  - Sheng, Kekai
AU  - Ding, Shouhong
AU  - Tai, Ying
AU  - Li, Jilin
AU  - Xie, Yuan
AU  - Ma, Lizhuang
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 867
EP  - 873
DO  - 10.24963/ijcai.2021/120
UR  - https://doi.org/10.24963/ijcai.2021/120
ER  -