Meta Anti-spoofing: Learning to Learn in Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Previously, most methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks (PA). However, new attack methods keep evolving that produce new forms of spoofing faces to compromise the existing detectors. This requires researchers to collect a large number of samples to train classifiers for detecting new attacks, which is often costly and leads the later newly evolved attack samples to remain in small scales. Alternatively, we define face anti-spoofing as a few-shot learning problem with evolving new attacks and propose a novel face anti-spoofing approach via meta-learning named Meta Face Anti-spoofing (Meta-FAS). Meta-FAS addresses the above-mentioned problems by training the classifiers how to learn to detect the spoofing faces with few examples. To assess the effectiveness of the proposed approach, we propose a series of evaluation benchmarks based on public datasets (\textit{e.g.}, OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, MSU-MFSD, 3D-MAD, and CASIA-SURF), and the proposed approach shows its superior performances to compared methods.