  Face anti-spoofing is the crucial step to prevent face recognition systems
from a security breach. Previous deep learning approaches formulate face
anti-spoofing as a binary classification 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. Then we fuse the estimated depth and rPPG to distinguish live vs.
spoof faces. In addition, we introduce a new face anti-spoofing database that
covers a large range of illumination, subject, and pose variations.
Experimental results show that our model achieves the state-of-the-art
performance on both intra-database and cross-database testing.
