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Broad Learning with Uniform Local Binary Pattern for Fingerprint Liveness Detection

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Neural Computing for Advanced Applications (NCAA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1637))

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Abstract

Recently, with the widespread application of mobile communication devices, fingerprint identification is the most prevalent in all types of mobile computing. While they bring a huge convenience to our lives, the resulting security and privacy issues have caused widespread concern. Fraudulent attack using forged fingerprint is one of the typical attacks to realize illegal intrusion. Thus, fingerprint liveness detection (FLD) for True or Fake fingerprints is very essential. This paper proposes a novel fingerprint liveness detection method based on broad learning with uniform local binary pattern (ULBP). Compared to convolutional neural networks (CNN), training time is drastically reduced. Firstly, the region of interest of the fingerprint image is extracted to remove redundant information. Secondly, texture features in fingerprint images are extracted via ULBP descriptors as the input to the broad learning system (BLS). ULBP reduces the variety of binary patterns of fingerprint features without losing any key information. Finally, the extracted features are fed into the BLS for training. The BLS is a flat network, which transfers and places the original input as a mapped feature in feature nodes, generalizing the structure in augmentation nodes. Experiments show that in Livdet 2011 and Livdet 2013 datasets, the average training time is about 1 s and the performance of identifying real and fake fingerprints is effect. Compared to other advanced models, our method is faster and more miniature.

C. Yuan and M. Chen—Contributed equally to this work and should be considered co-first authors.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China under grant 62102189; by the Jiangsu Basic Research Programs-Natural Science Foundation under grant BK20200807; by the Research Startup Foundation of NUIST under grant 2020r015; by the Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department under grant LGF21F020006; by the Key Laboratory of Public Security Information Application Based on Big-Data Architecture, Ministry of Public Security under grant 2021DSJSYS006; by NUDT Scientific Research Program under grant JS21-4; by the 2022 Excellent Undergraduate Graduation Design (Paper) support program of NUIST under grant 201983290123.

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Authors and Affiliations

  1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Mingyu Chen, Chengsheng Yuan & Zhili Zhou

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

    Mingyu Chen, Chengsheng Yuan & Zhili Zhou

  3. School of International Relations, National University of Defense Technology, Nanjing, 210044, China

    Xinting Li

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  1. Mingyu Chen
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  2. Chengsheng Yuan
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  4. Zhili Zhou
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Corresponding author

Correspondence to Chengsheng Yuan .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. University of Jinan, Jinan, China

    Yuehui Chen

  3. Shenzhen University, Shenzhen, China

    Xianghua Chu

  4. Hefei University of Technology, Hefei, China

    Zhao Zhang

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

  6. Chongqing University, Chongqing, China

    Zhou Wu

  7. Western University, London, ON, Canada

    Yimin Yang

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Chen, M., Yuan, C., Li, X., Zhou, Z. (2022). Broad Learning with Uniform Local Binary Pattern for Fingerprint Liveness Detection. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_25

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  • DOI: https://doi.org/10.1007/978-981-19-6142-7_25

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  • Print ISBN: 978-981-19-6141-0

  • Online ISBN: 978-981-19-6142-7

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