Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

Zhe Kong1,2; Wentian Zhang3; Tao Wang4; Kaihao Zhang5; Yuexiang Li6; Xiaoying Tang7; Wenhan Luo2,8

2024

10.1109/TCSVT.2024.3451294

IEEE Transactions on Circuits and Systems for Video Technology   影响因子和分区

1558-2205

Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Although many face anti-spoofing (FAS) methods perform well in intra-dataset scenarios, their generalization remains a challenge. To address this issue, some methods adopt domain adversarial training (DAT) to extract domain-invariant features. Differently, in this paper, we propose a domain adversarial attack (DAA) method by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment. Moreover, since models trained on limited data and types of attacks cannot generalize well to unknown attacks, we propose a dual perceptual and generative knowledge distillation framework for face anti-spoofing that utilizes pre-trained face-related models containing rich face priors. Specifically, we adopt two different face-related models as teachers to transfer knowledge to the target student model. The pre-trained teacher models are not from the task of face anti-spoofing but from perceptual and generative tasks, respectively, which implicitly augment the data. By combining both DAA and dual-teacher knowledge distillation, we develop a dual teacher knowledge distillation with domain alignment framework (DTDA) for face anti-spoofing. The advantage of our proposed method has been verified through extensive ablation studies and comparison with state-of-the-art methods on public datasets across multiple protocols.

其他

1.School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
2.Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
3.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
4.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
5.Harbin Institute of Technology, Shenzhen, China
6.Guangxi Medical University, Guangxi, China
7.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
8.Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

Zhe Kong,Wentian Zhang,Tao Wang,et al. Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing[J]. IEEE Transactions on Circuits and Systems for Video Technology,2024,PP(99).

Zhe Kong.,Wentian Zhang.,Tao Wang.,Kaihao Zhang.,Yuexiang Li.,...&Wenhan Luo.(2024).Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing.IEEE Transactions on Circuits and Systems for Video Technology,PP(99).

Zhe Kong,et al."Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing".IEEE Transactions on Circuits and Systems for Video Technology PP.99(2024).