Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation

Abstract

Spoofing attack is a catastrophic threat for biometric authentication systems. Inspired by the concept of depth map estimation, a novel anti-spoofing technique based on aggregated local weighted gradient orientation (ALWGO) is proposed. We first estimate the depth of the specimen face image. In the next step, highly discriminant ALWGO features are extracted from the depth map. Finally, a sparse representation classifier is trained to distinguish between the genuine and fake faces. This paper particularly addresses the potential of texture gradient features and their variations, on three types of attacks, viz. printed high-definition photographs, warped photographs and videos displayed on mobile phones. The usage of ALWGO features has been extended for further face recognition. Our proposed approach is robust and nonintrusive as compared to many existing methods. Extensive experimental analysis on publicly available databases clearly demonstrates the superiority of our approach for both face spoofing detection and recognition systems.