A face anti-spoofing method based on optical flow field
Wenze Yin, Yue Ming, Lei Tian · 2016
Spoofing attack can easily deceive face recognition system. In this paper, we explore the issue of face anti-spoofing with good performance in accuracy by utilizing optical flow vector on two types of attacks: photos and videos shown on high-resolution electronic screens. The key idea is to calculate the displacement of optical flow vector between two successive frames of a face video and obtain a displacement sum of a certain number of frames. Under the circumstance of stable light, the sum of displacement differs from real access and other spoofing attacks. In this situation, we experiment on REPLAY-ATTACK, a common and popular face spoofing database which shows a good performance. We conclude that spoofing attacks and real faces have different optical flow motion trend that our method shows temperate guesstimate when facing with a broad set of face attacks.