Abstract
With the wide deployment of the face recognition system, many face attacks, such as print attack, video attack and 3D face mask, have emerged. Face anti-spoofing is very important to protect face recognition system from attack. This paper proposes a structure of generative adversarial networks with skip connection for face anti-spoofing. First, we obtain the latent spatial features of faces by training generative adversarial networks to reconstruct both real and spoof faces; second, we use the convolution neural networks to detect the spoofing faces. In this paper, the proposed method is evaluated by three public databases. The results suggest that our approach achieves as high as 98% accuracy on both CASIA-FASD and REPLAY-ATTACK databases.
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Xia, J., Tang, Y., Jia, X., Shen, L., Lai, Z. (2019). Latent Spatial Features Based on Generative Adversarial Networks for Face Anti-spoofing. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_27
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