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Fingerprint Liveness Detection Based on Multi-modal Fine-Grained Feature Fusion

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Smart Multimedia (ICSM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12015))

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Abstract

Recently, fingerprint recognition systems are widely deployed in our daily life. However, spoofing via using special materials such as silica, gelatin, Play-Doh, clay, etc., is one of the most common methods of attacking fingerprint recognition systems. To handle the above defects, a fingerprint liveness detection (FLD) technique is proposed. In this paper, we propose a novel structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and make full use of each algorithm, this paper extracts three types of different fine-grained texture features, such as SIFT, LBP, HOG. Next, we developed a feature fusion rule, including five fusion operations, to better integrate the above features. Finally, those fused features are fed into an SVM classifier for the subsequent classification. Experimental results on the benchmark LivDet 2013 fingerprints indicate that the classification performance of our method outperforms other FLD methods proposed in recent literature.

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Acknowledgements

The authors are grateful for the anonymous reviewers who made valuable comments and improvements. Furthermore, many thanks to Xinting Li and Weijin Cheng for helping us polish the article and make suggestions. This research was funded by the Canada Research Chair Program and the NSERC Discovery Grant; by the Startup Foundation for Introducing Talent of NUIST (2020r015); 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, China

    Chengsheng Yuan

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

    Chengsheng Yuan & Q. M. Jonathan Wu

Authors
  1. Chengsheng Yuan
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  2. Q. M. Jonathan Wu
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Corresponding author

Correspondence to Chengsheng Yuan .

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Editors and Affiliations

  1. Arizona State University, Mesa, AZ, USA

    Troy McDaniel

  2. University of Florence, Florence, Italy

    Stefano Berretti

  3. Nokia Technologies, Tampere, Finland

    Igor D. D. Curcio

  4. University of Alberta, Edmonton, AB, Canada

    Anup Basu

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Yuan, C., Jonathan Wu, Q.M. (2020). Fingerprint Liveness Detection Based on Multi-modal Fine-Grained Feature Fusion. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-54407-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54406-5

  • Online ISBN: 978-3-030-54407-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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