  Face anti-spoofing (FAS) plays a vital role in securing face recognition
systems from the presentation attacks (PAs). As more and more realistic PAs
with novel types spring up, it is necessary to develop robust algorithms for
detecting unknown attacks even in unseen scenarios. However, deep models
supervised by traditional binary loss (e.g., `0' for bonafide vs. `1' for PAs)
are weak in describing intrinsic and discriminative spoofing patterns.
Recently, pixel-wise supervision has been proposed for the FAS task, intending
to provide more fine-grained pixel/patch-level cues. In this paper, we firstly
give a comprehensive review and analysis about the existing pixel-wise
supervision methods for FAS. Then we propose a novel pyramid supervision, which
guides deep models to learn both local details and global semantics from
multi-scale spatial context. Extensive experiments are performed on five FAS
benchmark datasets to show that, without bells and whistles, the proposed
pyramid supervision could not only improve the performance beyond existing
pixel-wise supervision frameworks, but also enhance the model's
interpretability (i.e., locating the patch-level positions of PAs more
reasonably). Furthermore, elaborate studies are conducted for exploring the
efficacy of different architecture configurations with two kinds of pixel-wise
supervisions (binary mask and depth map supervisions), which provides
inspirable insights for future architecture/supervision design.
