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Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection

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Abstract

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authentication systems is that most fingerprint scanners are vulnerable to presentation attacks by artificial replicas, made from plastic clay, gelatin, silicon, wood glue, etc. One anti-spoofing attack scheme, called real-time fingerprint liveness detection (RFLD), has been proposed to discriminate live or fake fingerprints. Currently, to resolve the presentation attacks, most RFLD solutions all relied on handcrafted feature extraction and selection. The features extracted by manual method are shallow features of the samples; however, autoencoder can automatically learn deep hierarchical semantic features representation of the samples, thus replacing the operations extracted with hand-designed features. In this paper, we apply stacked autoencoder to RFLD to significantly lower the work-force burden of the feature extraction engineering, and our model consists of two parts: parameter pre-training based on unsupervised learning and FLD based on supervised learning. The performance has been verified on two public fingerprint datasets: LivDet 2011 and 2013, and the experimental results indicate that our proposed approach works well for RFLD as well as the detection performance is satisfactory.

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Acknowledgements

The authors are grateful for the anonymous reviewers who made constructive comments and improvements. This work is supported by the Canada Research Chair Program and the NSERC Discovery Grant; by the National Natural Science Foundation of China under Grant U1836208, U1536206, U1836110, 61602253, 61672294; by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407; by the National Key R& D Program of China under grant 2018YFB1003205; by the Jiangsu Postgraduate Research and Innovation Program under Grant KYCX17_0899; by the State Scholarship fund 201708320316, China; by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund; by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China.

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Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chengsheng Yuan, Xianyi Chen, Peipeng Yu, Ruohan Meng, Weijin Cheng, Q. M. Jonathan Wu & Xingming Sun

  2. Jiangsu Engineering Center of Network Monitoring, Nanjing, 210044, China

    Chengsheng Yuan, Xianyi Chen, Peipeng Yu, Ruohan Meng, Weijin Cheng, Q. M. Jonathan Wu & Xingming Sun

  3. Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

    Chengsheng Yuan & Q. M. Jonathan Wu

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  1. Chengsheng Yuan
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Correspondence to Chengsheng Yuan or Xingming Sun.

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Yuan, C., Chen, X., Yu, P. et al. Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection. J Real-Time Image Proc 17, 55–71 (2020). https://doi.org/10.1007/s11554-019-00928-0

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