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Finger Knuckleprint Based Recognition System Using Feature Tracking

  • Conference paper
Biometric Recognition (CCBR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7098))

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Abstract

This paper makes use of finger knuckleprints to propose an efficient biometrics system. Edge based local binary pattern (ELBP) is used to enhance the knuckleprint images. Highly distinctive texture patterns from the enhanced knuckleprint images are extracted for better classification. It has proposed a distance measure between two knuckleprint images. This system has been tested on the largest publicly available Hong Kong Polytechnic University (PolyU) finger knuckleprint database consisting 7920 knuckleprint images of 165 distinct subjects. It has achieved CRR of more than 99.1% for the top best match, in case of identification and ERR of 3.6%, in case of verification.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Aditya Nigam & Phalguni Gupta

Authors
  1. Aditya Nigam
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  2. Phalguni Gupta
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Center for Biometrics and Security Research, Chinese Academy of Sciences, Institute of Automation, P.O. Box 2728, 100190, Beijing, China

    Zhenan Sun  & Tieniu Tan  & 

  2. School of Information Science and Technolog, Sun Yat-Sen University, 510275, Guangzhou, China

    Jianhuang Lai

  3. Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China

    Xilin Chen

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© 2011 Springer-Verlag Berlin Heidelberg

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Nigam, A., Gupta, P. (2011). Finger Knuckleprint Based Recognition System Using Feature Tracking. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-25449-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25448-2

  • Online ISBN: 978-3-642-25449-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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