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上传时间: 2019-03-19
详细说明： Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth super- vised 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 est imate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently en- codes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated re- current units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demon- strate 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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