Hybrid feature descriptors to detect face spoof attacks

Abstract

Face Recognition is widely used applications such as of mobile phone unlocking, credit card authentication and person authentication in airports. The face biometric authentication system can be easily spoofed by printed photograph, replay video of the legitimate user and 3D face mask. This paper proposes hybrid feature descriptors to detect the face spoofing attack (printed photograph and replay video attacks). The proposed method extracts three different feature descriptors such as Color moment, Haralick texture and Color Local Binary Pattern (CLBP) feature descriptors. The extracted features are concatenated and classified by Logistic Regression. The performance of the proposed method is evaluated on the Michigan State University Mobile Face Spoofing Database (MSU-MFSD) dataset and found to achieve better results than state-of-the-art methods.