Face spoofing detection based on chromatic ED-LBP texture feature

Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy information is encoded into LBP. This novel texture feature extraction method has the advantage of getting more elaborate texture information without increasing the feature dimension. Second, the texture signatures in different color channels are fully investigated. More specifically, the feature histograms are calculated over each image band separately. Third, by the integration of the chromatic ED-LBP histograms and the two-level spatial pyramid, the local structure information of face is encoded in our approach which can well describe the differences between facial videos of valid users and impostors. Finally, the ED-LBP histograms from different color spaces are usually cascaded into a united feature vector which is feed into SVM for classification identification. Extensive experiments on four challenging and publicly available face anti-spoofing databases, namely CASIA FASD, Replay-Attack, Replay-Mobile, and OULU-NPU, demonstrate the effectiveness of our proposed approach. The results indicate that our methods are superior to state-of-the-art techniques and can effectively resist photo and video spoofing attacks in face recognition (FR).