  Face authentication on mobile end has been widely applied in various
scenarios. Despite the increasing reliability of cutting-edge face
authentication/verification systems to variations like blinking eye and subtle
facial expression, anti-spoofing against high-resolution rendering replay of
paper photos or digital videos retains as an open problem. In this paper, we
propose a simple yet effective face anti-spoofing system, termed Aurora Guard
(AG). Our system firstly extracts the normal cues via light reflection
analysis, and then adopts an end-to-end trainable multi-task Convolutional
Neural Network (CNN) to accurately recover subjects' intrinsic depth and
material map to assist liveness classification, along with the light CAPTCHA
checking mechanism in the regression branch to further improve the system
reliability. Experiments on public Replay-Attack and CASIA datasets demonstrate
the merits of our proposed method over the state-of-the-arts. We also conduct
extensive experiments on a large-scale dataset containing 12,000 live and
diverse spoofing samples, which further validates the generalization ability of
our method in the wild.
