  In this paper, we propose a light reflection based face anti-spoofing method
named Aurora Guard (AG), which is fast, simple yet effective that has already
been deployed in real-world systems serving for millions of users.
Specifically, our method first extracts the normal cues via light reflection
analysis, and then uses an end-to-end trainable multi-task Convolutional Neural
Network (CNN) to not only recover subjects' depth maps to assist liveness
classification, but also provide the light CAPTCHA checking mechanism in the
regression branch to further improve the system reliability. Moreover, we
further collect a large-scale dataset containing $12,000$ live and spoofing
samples, which covers abundant imaging qualities and Presentation Attack
Instruments (PAI). Extensive experiments on both public and our datasets
demonstrate the superiority of our proposed method over the state of the arts.
