DOG-ADTCP: A new feature descriptor for protection of face identification system

Abstract

Nowadays the face biometric system is gaining popularity for authentication in access control applications. At the same time, the threats to authentication systems are also increased in terms of spoofing or presentation attacks where an intruder attempts to spoof the face recognition system by using genuine user photos or video to gain access. In order to effectively secure the secrecy of a genuine user, there is an urgent need for building a face authentication system with anti-spoofing countermeasures. In this paper, we introduce a novel face anti-spoofing approach, which is mainly based on contrast and texture features of both real and spoof photos. A modified Difference of Gaussian (DOG) filtering method and Angle Directional Ternary Co-relation pattern (ADTCP) are used. The publicly available NUAA, MSU-MFSD and REPLAY-ATTACK photo-impostor datasets are tested on the approach, which includes images with different illumination and area of the face. The accuracy of the proposed approach is evaluated using different metrics. The results show that our proposed method is superior to other state-of-the-art (SOTA) practices when tested on three photo-impostor datasets.