Exploiting temporal and depth information for multi-frame face anti-spoofing
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Face anti-spoofing is significant to the security of face recognition systems. By utilizing pixel-wise supervision, depth supervised face anti-spoofing reasonably contains more generalization than binary classification does. Without considering the importance of sequential information in depth recovery, previous depth supervised methods only regard depth as an auxiliary supervision in the single frame. In this paper, we propose a depth supervised face anti-spoofing model in both spatial and temporal domains. The temporal information from multi-frames is exploited and incorporated to improve facial depth recovery, so that more robust and discriminative features can be extracted to classify the living and spoofing faces. Extensive experiments indicate that our approach achieves state-of-the-art results on standard benchmarks.
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