关键词: 关键词：人脸活体检测, 伪源域, 对比学习, 领域泛化

Abstract: Abstract: Face anti-spoofing （FAS） is an important mean to guarantee the security of face recognition systems. Existing FAS methods have poor generalization in cross-dataset testing scenarios， which leads to a drastic performance degradation. To this way， this paper proposes a FAS method based on domain synthesis and contrastive learning. The proposed method mainly contains two novel modules: domain synthesis module and contrastive learning module. The former randomly swaps local regions of face images in different source domains at the image level to generate pseudo-source domain samples. Then， the above face images are reconstructed at the feature-level by exchanging the local features of the corresponding positions reconstructed at the image-level. By maximizing the similarity of the reconstructed sample’s features and the reconstructed features， this module can ensure the stability of the generated pseudo source domain while expanding the number of samples and attack types. This provides a solid data foundation for the proposed method to learn generalized feature spaces. The later minimizes the intra-class distance of the real face representation and maximizes the inter-class distance between the real face and spoof faces. Meanwhile， this module maximizes the inter-class distance between the real face and the reconstructed samples. This process effectively promotes intra-class compactness of the real face and ensures that the proposed method can learn a good decision curve. The proposed method is trained and tested on four publicly available face live detection datasets CASIA-FASD， Replay-Attack， MSU-MFSD， and OULU-NPU， and the experimental results show that the proposed method has a good generalization performance in cross-dataset testing scenarios.

Key words: Key words: face anti-spoofing, pseudo source domain, contrastive learning, domain generalization