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arXiv · 1811.05118

Exploiting temporal and depth information for multi-frame face anti-spoofing

Zezheng Wang·Chenxu Zhao·Yunxiao Qin·Qiusheng Zhou·Guojun Qi·Jun Wan·Zhen Lei
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Abstract

Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.

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Zezheng Wang, Chenxu Zhao, Yunxiao Qin, Qiusheng Zhou, Guojun Qi, Jun Wan, Zhen Lei. 2018-11-13. Exploiting temporal and depth information for multi-frame face anti-spoofing. https://arxiv.org/abs/1811.05118

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