Surveillance Face Anti-spoofing

• URL: http://arxiv.org/abs/2301.00975v1
• Date: Tue, 3 Jan 2023 07:09:57 GMT
• Title: Surveillance Face Anti-spoofing
• Authors: Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang, Stan Z. Li, Zhen Lei
• Abstract summary: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality. A large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
• Score: 81.50018853811895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

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