Adversarial learning and decomposition-based domain generalization for face anti-spoofing
Liu, Mingxin; Mu, Jiong; Yu, Zitong; Ruan, Kun; Shu, Baiyi; Yang, Jie (2021-10-14)
Liu, M., Mu, J., Yu, Z., Ruan, K., Shu, B., & Yang, J. (2022). Adversarial learning and decomposition-based domain generalization for face anti-spoofing. Pattern Recognition Letters, 155, 171–177. https://doi.org/10.1016/j.patrec.2021.10.014
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe202201101661
Tiivistelmä
Abstract
Face anti-spoofing (FAS) plays a critical role in the face recognition community for securing the face presentation attacks. Many works have been proposed to regard FAS as a domain generalization problem for robust deployment in real-world scenarios. However, existing methods focus on extracting intrinsic spoofing cues to improve the generalization ability, yet neglect to train a robust classifier. In this paper, we propose a framework to improve the generalization ability of face anti-spoofing in two folds:) a generalized feature space is obtained via aggregation of all live faces while dispersing each domain’s spoof faces; and) a domain agnostic classifier is trained through low-rank decomposition. Specifically, a Common Specific Decomposition for Specific (CSD-S) layer is deployed in the last layer of the network to select common features while discarding domain-specific ones among multiple source domains. The above-mentioned two components are integrated into an end-to-end framework, ensuring the generalization ability to unseen scenarios. The extensive experiments demonstrate that the proposed method achieves state-of-the-art results on four public datasets, including CASIA-MFSD, MSU-MFSD, Replay-Attack, and OULU-NPU.
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