Liveness Identity Verification for Face Anti-Spoofing in Biometric Validation using Recurrent Neural Network

Face anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person's face. It has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. However, most previous approaches still suffer from diverse types of spoo？ng attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method is proposed based on liveness identity verification for face anti-spoo？ng in biometric validation using the Recurrent Neural Network (RNN).