Recently, one paper on cross-domain face presentation attack detection is accepted by the journal IEEE TIFS . The full name of IEEE TIFS is IEEE Transactions on Information Forensics and Security, which is a CCF-A journal on biometrics and information security with an impact factor of 6.211 announced in 2019. The paper information is as follows:
Guoqing Wang, Hu Han, Shiguang Shan, and Xilin Chen, “Unsupervised Adversarial Domain Adaptation for Cross-domain Face Presentation Attack Detection”, IEEE Transactions on Information Forensics and Security, 2020. (Accepted)
Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. As shown in the figure below, DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.
 Guoqing Wang, Hu Han, Shiguang Shan, and Xilin Chen. Unsupervised Adversarial Domain Adaptation for Cross-domain Face Presentation Attack Detection, IEEE Transactions on Information Forensics and Security (T-IFS). (Accepted on June. 10, 2020)
 Guoqing Wang, Hu Han, Shiguang Shan, and Xilin Chen. Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
 Guoqing Wang, Hu Han, Shiguang Shan, and Xilin Chen. Improving Cross-database Face Presentation Attack Detection via Adversarial Domain Adaptation, in Proc. 12th IAPR International Conference on Biometrics (ICB), pp. 1-8, Crete, Greece, Jun 4-7, 2019.