文件名称：炫光活体人脸检测.pdf
文件大小：3.1MB
文件格式：GZ
更新时间：2022-04-08 09:17:07
深度学习 人工智能
Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning ap- proaches formulate face anti-spoofing as a binary classifi- cation problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we ar- gue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues. A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision. The estimated depth and rPPG are fused to distinguish live vs. spoof faces. Further, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations. Experiments show that our model achieves the state-of-the- art results on both intra- and cross-database testing.
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炫光活体人脸检测.pdf