Disentangled Representation based Face Anti-Spoofing

Abstract

Face anti-spoofing is an important problem for both academic research and industrial face recognition systems. Most of the existing face anti-spoofing methods take it as a classification task on individual static images, where motion pattern differences in consecutive real or fake face sequences are ignored. In this work, we propose a novel method to identify spoofing patterns using motion information. Different from previous methods, the proposed method makes the real or fake decision on the disentangled feature level, based on the observation that motion and spoofing pattern features could be disentangled from original image frames. We design a representation disentangling framework for this task, which is able to reconstruct both real and fake face sequences from the input. Meanwhile, the disentangled representations could be used to classify whether the input faces are real or fake. We perform several experiments on public face anti-spoofing datasets. The proposed method achieves SOTA results compared with existing methods.