Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection

With their growing popularity and widespread applications, face recognition systems are attracting more attention from attackers. Thus, face presentation attack detection has emerged as an important research topic in recent years. Existing methods for face presentation attack detection are affected by different cameras and display devices, and their performance is degraded in cross-database testing. In this paper, we propose a face presentation attack detection scheme that fuses multi-perspective dynamic features. One feature is the globally extracted temporal motion pattern of a face in a video. This involves mapping the local and global motion information of the face in the video into a single image. The motion patterns of genuine and fake faces are different, and these patterns are independent of cameras and display devices. Another feature is the visual rhythm of noise patterns, which differs significantly between single and secondary imaging. The proposed scheme fuses these two features at the decision level. Cross-database tests were conducted among the CASIA-FASD, MSU-MFSD and Replay-Attack databases. The experimental results show that the proposed scheme outperforms state-of-the-art algorithms.