Surveillance Face Anti-spoofing

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 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.