Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning

By Guoqing Wang, Hu Han, Shiguang Shan, Xilin Chen. 2020

205 citations recorded by the source corpus.

Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chen Change Loy
2. Yu Qiao
3. Luc Van Gool
4. Xiaogang Wang
5. Ziwei Liu
6. Tao A. Xiang
7. Dacheng Tao
8. Ming-Hsuan Yang

In Safety, Robustness & Interpretability

1. Yu Qiao
2. Tao A. Xiang
3. Chen Change Loy
4. Guoying Zhao
5. Ziwei Liu
6. Kaiyang Zhou

In Signal Processing

1. Chen Change Loy
2. Yu Qiao
3. Tao A. Xiang
4. Xiaobai Li
5. Guoying Zhao
6. Zitong Yu
7. Zhilin Yang
8. Ziwei Liu

Citations in the corpus

• ZeroPrompt: Scaling Prompt-Based Pretraining to 1, 000 Tasks Improves Zero-Shot Generalization (2022, background): “, 2021) or disentanglement learning (Wang et al., 2020; Peng et al., 2020, 2019).”
• Face Anti-Spoofing with Human Material Perception (2020, background): “In order to learn generalized features for unseen attacks and environment, few-shot learning [8], zero-shot learning [8,29] and domain generalization [30,31,32] are introduced for FAS task.”
• Domain Generalization: A Survey (2021, methodology): “The existing approaches falling into this group are either based on decomposition [20], [36], [184], [185] or generative modeling [40], [186].”