Improving Face Anti-Spoofing via Advanced Multi-Perspective Feature Learning

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Previous approaches usually learn spoofing features from single perspective, which only universal cues shared by all attack types are explored. However, such single-perspective-based ignore the differences among various attacks and commonness between certain bona fides, thus tending to neglect some non-universal that contain strong discernibility against types. As result, when dealing with multiple of attacks, above may suffer uncomprehensive representation fides spoof faces. In this work, we propose novel Advanced Multi-Perspective Feature Learning network (AMPFL), perspectives adopted discriminative features, improve performance FAS. Specifically, proposed first learns several perspective-specific perspectives, then aggregates further enhances them perform anti-spoofing. way, AMPFL obtains difficult be captured methods provides more comprehensive information on faces, achieving better for Experimental results show our achieves promising public databases, it effectively solves issues approaches.