%0 Generic
%T Surveillance Face Anti-spoofing
%A Hao Fang
%A Ajian Liu
%A Jun Wan
%A Sergio Escalera
%A Chenxu Zhao
%A Xu Zhang
%A Stan Z Li
%A Zhen Lei
%D 2023
%F Hao Fang2023
%O HUPBA
%O exported from refbase (http://158.109.8.37/show.php?record=3869), last updated on Wed, 10 Jan 2024 15:18:00 +0100
%X 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.
%9 miscellaneous
%U https://arxiv.org/abs/2301.00975
%U http://158.109.8.37/files/FLW2023.pdf