Title: Face spoof detection using feature map superposition and CNN

Authors: Fei Gu; Zhihua Xia; Jianwei Fei; Chengsheng Yuan; Qiang Zhang

Addresses: Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

Abstract: Face biometrics have been widely applied for user authentication systems in many practical scenarios, but the security of these systems can be jeopardised by presenting photos or replays of the legitimate user. To deal with such threat, many handcraft features extracted from face images or videos were used to detect spoof faces. These methods mainly analysed either illumination differences, colour differences or textures differences, but did not fusion these features together to further improve detection performance. Thus in this paper, we propose a novel face spoof detection method based on various feature maps and convolution neural network for photo and replay attacks. Specifically, both facial contour and specularly reflected features are considered, and proposed network is task oriented designed, e.g., its depth and width, and specific convolutional parameters of each layer are chosen for optimal accuracy and efficiency. A remarkable performance through plenty of experiments on multiple datasets shows that our method can defend not only photo attack, but also replay attack with a very low error probability.

Keywords: face spoof detection; convolution neural network; difference of Gaussians; specular reflected light.

DOI: 10.1504/IJCSE.2020.107356

International Journal of Computational Science and Engineering, 2020 Vol.22 No.2/3, pp.355 - 363

Received: 29 Jul 2019
Accepted: 19 Aug 2019
Published online: 18 May 2020 *

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