Face Anti-Spoofing with Human Material Perception
By Zitong Yu, Xiaobai Li, Xuesong Niu, Jingang Shi, Guoying Zhao. 2020
155 citations recorded by the source corpus.
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 [1], 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$\pm$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.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
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In Computer Vision and Pattern Recognition
In Information Systems
In Signal Processing
- Zhen Lei
- S. Li
- Xiangyu Zhu
- Mohammad Rostami
- Mohamed E. Hussein
- Wael AbdAlmageed
- Leonidas Spinoulas
- Yang Yang
Citations in the corpus
- Detection and Continual Learning of Novel Face Presentation Attacks (2021, background): “Similar to most sub-fields in computer vision, advances in deep learning, which is inspired by the nervous system [17], have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [1, 7, 12, ”
- Towards Fast, Accurate and Stable 3D Dense Face Alignment (2020, background): “, recognition [42,6,25,23,12], animation [9], avatar retargeting [8], tracking [47], attribute classification [3,21,20], image restoration [48,11,10], anti-spoofing [46,51,38,50,24].”