Surveillance Face Anti-spoofing
Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang,, Stan Z. Li, Zhen Lei

TL;DR
This paper introduces a large-scale surveillance face anti-spoofing dataset and a novel contrastive learning network to improve detection accuracy in low-quality, long-distance surveillance scenarios.
Contribution
The paper presents the SuHiFiMask dataset capturing diverse surveillance scenes and proposes the CQIL network with quality-invariance modules for robust face anti-spoofing.
Findings
SuHiFiMask dataset effectively captures real surveillance conditions.
CQIL outperforms existing methods in low-quality scenarios.
Contrastive learning enhances robustness against image quality variations.
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…
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Taxonomy
TopicsBiometric Identification and Security · Face recognition and analysis · Reconstructive Facial Surgery Techniques
MethodsContrastive Learning
