Face anti-spoofing is an important task to assure the security of face recognition systems. To be applicable to unconstrained real-world environments, generalization capabilities of the face anti-spoofing methods are required. In this work, we present a face anti-spoofing method with robust generalization ability to unseen environments. To achieve our goal, we suggest bipartite auxiliary supervision to properly guide networks to learn generalizable features. We propose a bipartite auxiliary supervision network(BASN) that comprehensively utilizes the suggested supervision to accurately detect presentation attacks. We evaluate our method by conducting experiments on public benchmark datasets and we achieve state-of-the-art performances.
[ Figure 1 ] The proposed architecture of BASN