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

Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection