Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the spoofing faces. All these cues are based on the discrepancy among physical materials (e.g., skin, glass, paper and silicone). In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception, intending to extract discriminative and robust features for FAS. To this end, we propose the Bilateral Convolutional Networks (BCN), which is able to capture intrinsic material-based patterns via aggregating multi-level bilateral macro- and micro- information. Furthermore, Multi-level Feature Refinement Module (MFRM) and multi-head supervision are utilized to learn more robust features. Comprehensive experiments are performed on six benchmark datasets, and the proposed method achieves superior performance on both intra- and cross-dataset testings. One highlight is that we achieve overall 11.3 ± 9.5% EER for cross-type testing in SiW-M dataset, which significantly outperforms previous results. We hope this work will facilitate future cooperation between FAS and material communities.
Yu Zitong, Li Xiaobai, Niu Xuesong, Shi Jingang, Zhao Guoying
A4 Article in conference proceedings
Place of publication:
Computer Vision – ECCV 2020 16th European Conference Proceedings, Part VII, Glasgow, UK, August 23–28, 2020
Yu Z., Li X., Niu X., Shi J., Zhao G. (2020) Face Anti-Spoofing with Human Material Perception. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_33
Read the publication here: