Face liveness detection and recognition using shearlet based feature descriptors
Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by non-real faces such as a photograph or video of valid user. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to develop a multifunctional feature descriptor and an efficient framework which can be used to deal with both face liveness detection and recognition. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and softmax classifier are concatenated to detect face liveness and identify person. We evaluated this approach using CASIA Face Anti-Spoofing Database and the results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of this database, and is possible to significantly enhance the security of face recognition biometric system.