Multimodal Face Spoofing Detection via RGB-D Images
Xudong Sun, Lei Huang, Changping Liu · 2018
While it has been shown that using 3D information might significantly benefit face anti-spoofing systems, traditional color images are still generally used, due to several issues such as expensive hardware requirement, high time cost, or poor accessibility when obtaining and using true 3D images. Thus, we could use RGB-D images captured by relatively low cost sensors instead, e.g., Kinect cameras, to achieve better performance without consuming huge amount of time or money. This research presents a novel multimodal face anti-spoofing method, which makes full use of available information on RGB-D images and no manually chosen regions are needed. For every pair of RGB-D images, first of all, we calculate the correlation between color and depth images to detect multimodal properties; then, by analyzing the consistency of sub regions extracted from the depth image, we are able to distinguish flat spoofing faces from genuine human beings. Both anti-spoofing features are fused to make final anti-spoofing decisions. Experiments on both self-collected and pubic 3DMAD datasets show that our proposed approach is effective for intra-dataset and cross-dataset testing scenarios, and that our method could deal with different presentation attacks carried by photos, tablet screens, and face masks.