Abstract
Face anti-spoofing is crucial to prevent face recognition
systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classifi-
cation problem. Many of them struggle to grasp adequate
spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the
learning toward discriminative and generalizable cues. A
CNN-RNN model is learned to estimate the face depth with
pixel-wise supervision, and to estimate rPPG signals with
sequence-wise supervision. The estimated depth and rPPG
are fused to distinguish live vs. spoof faces. Further, we
introduce a new face anti-spoofing database that covers a
large range of illumination, subject, and pose variations.
Experiments show that our model achieves the state-of-theart results on both intra- and cross-database testing