A novel face anti-spoofing method using multiple color space models
Tingting Li, Lian Zhichao · 2021
The importance of face recognition algorithms in biometric authentication systems has become increasingly prominent. In order to ensure the security of face authentication, it is crucial to detect spoof attacks before performing face recognition. In this paper, we propose a 9-layer convolutional neural network (CNN) architecture using end-to-end learning for face anti-spoofing applications in small-scale datasets, which can directly judge the corresponding output class of the raw input face image. In addition, we believe that real faces and fake faces are well distinguishable in color spaces other than the RGB space. Therefore, we propose a novel face anti-spoofing method using multiple color space models to provide complementary features. Extensive experiments on mixed dataset for CASIA-FASD database and Replay-Attack database showed excellent face spoofing detection result comparing with other similar approaches.