Research on a Fingerprint Liveness Detection Algorithm Based on Deep Convolution Neural Networks

doi:10.3969/j.issn.1671-1122.2018.06.004

Abstract

Abstract:

With the wide application of fingerprint authentication system in recent years, forged fingerprint detection has been paid more and more attentions. Based on the application characteristics of convolutional neural network in the fields of computer vision, face recognition and image classification, this paper proposes a fingerprint liveness detection algorithm called F-net. The algorithm uses BN layer, inception structure and global mean pool level to optimize the network, so as to reduce the large number of parameters in F-net network and the computational complexity. This also makes the algorithm get a higher recognition rate when the algorithm uses a large learning rate to train the network. Many algorithms are tested on LivDet2011 and LivDet2013 datasets. The experimental results show that F-net has high recognition rate and real-time detection performance.

Key words: fingerprint liveness detection, fingerprint anti-spoofing, convolution neural network, inception structure

CLC Number:

TP309

Min LONG, Xiaohai LONG, Li MA. Research on a Fingerprint Liveness Detection Algorithm Based on Deep Convolution Neural Networks[J]. Netinfo Security, 2018, 18(6): 28-35.

Figures/Tables 11

References 23

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