Multilevel fusing paired visible light and near-infrared spectral images for face anti-spoofing

Abstract

Face anti-spoofing has become a vital element for guaranteeing the security of face recognition systems. Previous face anti-spoofing approaches generally exploit cues in visible light images or near-infrared images individually. Few studies pay attention to fusing visible light and near-infrared images for face anti-spoofing. However, the strengths and weaknesses of visible light images and near-infrared images for face anti-spoofing can be complementary. In this study, we introduce a new face anti-spoofing dataset named as CIGIT-PPM, which includes paired visible light and near-infrared images with spoofing medium, distance, pose, expression and session variations for both print and 3D mask attacks. Further, we propose a novel fusing paired visible light and near-infrared spectral images CNN based approach for face anti-spoofing, combining the discriminating ability of both visible light and near-infrared spectral images. Specifically, an end-to-end CNN is employed to learn fusing representation from paired images and do classification at multilevel, and then weight averaged strategy is utilized to integrate the classification probability of different levels, which gives the final result that the input paired images are captured from a live face or a spoof face. Extensive experiments show that our method achieves state-of-the-art results on self-collected dataset CIGIT-PPM and public dataset msspoof.