face anti-spoofing based on color texture analysis
This work addresses the problem of improving security in face recognition systems against spoofing attacks, representing an incremental advancement by leveraging color data.
The paper tackled face spoofing detection by incorporating chrominance information, which was previously overlooked, using color texture analysis and achieved excellent results on benchmark datasets with promising generalization capabilities.
Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.