Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing

Authors

  • Zhihong Chen Zhejiang University Tencent, YouTu
  • Taiping Yao Tencent, YouTu
  • Kekai Sheng Tencent, YouTu
  • Shouhong Ding Tencent, YouTu
  • Ying Tai Tencent, YouTu
  • Jilin Li Tencent, YouTu
  • Feiyue Huang Tencent, YouTu
  • Xinyu Jin Zhejiang University

Keywords:

Biometrics, Face, Gesture & Pose

Abstract

Face anti-spoofing approach based on domain generalization (DG) has drawn growing attention due to its robustness for unseen scenarios. Existing DG methods assume that the domain label is known. However, in real-world applications, the collected dataset always contains mixture domains, where the domain label is unknown. In this case, most of existing methods may not work. Further, even if we can obtain the domain label as existing methods, we think this is just a sub-optimal partition. To overcome the limitation, we propose domain dynamic adjustment meta-learning (D$^2$AM) without using domain labels, which iteratively divides mixture domains via discriminative domain representation and trains a generalizable face anti-spoofing with meta-learning. Specifically, we design a domain feature based on Instance Normalization (IN) and propose a domain representation learning module (DRLM) to extract discriminative domain features for clustering. Moreover, to reduce the side effect of outliers on clustering performance, we additionally utilize maximum mean discrepancy (MMD) to align the distribution of sample features to a prior distribution, which improves the reliability of clustering. Extensive experiments show that the proposed method outperforms conventional DG-based face anti-spoofing methods, including those utilizing domain labels. Furthermore, we enhance the interpretability through visualization.

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Published

2021-05-18

How to Cite

Chen, Z., Yao, T., Sheng, K., Ding, S., Tai, Y., Li, J., Huang, F., & Jin, X. (2021). Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1132-1139. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16199

Issue

Section

AAAI Technical Track on Computer Vision I