ML Anthology

Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

Rui Shao, Xiangyuan Lan, Jiawei Li, Pong C. Yuen
CVPR 2019
doi:10.1109/CVPR.2019.01026 /cvpr/2019/shao2019cvpr-multiadversarial/

Abstract

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

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Cite

Text

Shao et al. "Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.01026

Markdown

[Shao et al. "Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/shao2019cvpr-multiadversarial/) doi:10.1109/CVPR.2019.01026

BibTeX

@inproceedings{shao2019cvpr-multiadversarial,
  title     = {{Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection}},
  author    = {Shao, Rui and Lan, Xiangyuan and Li, Jiawei and Yuen, Pong C.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.01026},
  url       = {https://mlanthology.org/cvpr/2019/shao2019cvpr-multiadversarial/}
}