Study on using individual differences in facial expressions for a face recognition system immune to spoofing attacks

Abstract

Although current face recognition systems in biometrics field are accurate enough to be used as substitutes for passwords or keys, most of them are prone to face spoofing attacks. Different techniques for face spoofing identification have been researched but most of them introduce additional sensors and are not cost or computationally efficient. In this study, the authors study the possibility of using individual differences in facial expressions for improving a face recognition system and make it immune to spoofing attacks. The authors develop a soft biometric neural-network-based system for video-based face recognition by analysing patterns in individual facial expressions on multiple frames. Results show that such a system is possible and has accuracies higher than 85%. Used alongside with a standard principal component analysis-based face recognition system, the combined method achieved 94.5% accuracy on Honda/UCSD Video Database and 92.9% on Youtube Faces DB, comparable with state-of-the-art. When tested against photo spoofing attacks on three public anti-spoofing databases the proposed method was immune. In terms of video spoofing, the error rate for the authors' proposed method was 1% surpassing state-of-the-art methods.