@inproceedings{0f90d382a7aa43f487b39c122db98692,
title = "Multi-adversarial discriminative deep domain generalization for face presentation attack detection",
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.",
keywords = "Biometrics, Vision Applications and Systems",
author = "Rui Shao and Xiangyuan Lan and Jiawei Li and Yuen, {Pong C.}",
note = "Funding Information: This project is partially supported by Hong Kong RGC GRF HKBU12201215. The work of X. Lan is partially supported by HKBU Tier 1 Start-up Grant.; 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CVPR.2019.01026",
language = "English",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "10015--10023",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019",
address = "United States",
}