An Effective Face Anti-Spoofing Method via Stereo Matching

Various algorithms based on Convolutional Neural Network (CNN) have achieved great performance in the task of face anti-spoofing (FAS). However, the issue of most approaches is that the robustness in unknown scenes is not strong due to the different quality of attack images and environmental factors. In this letter, we propose a real-time face anti-spoofing method based on stereo matching. We input the left and right views of a pair of infrared face images into our proposed lightweight stereo matching network to get a disparity map. Then, we use the disparity map as input for the classification network to obtain face living information. Experimental results show that the proposed approach has significantly improved performance compared with the latest state-of-the-art face anti-spoofing methods. Considering the real-time requirement, our method is superior to most single-image-based models in inference time and far less than those based on other large scale stereo matching networks in computational complexity. The code is available at
https://github.com/lizhishan1997/StereoMatching_FAS
.