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Iris Anti-spoofing

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Book cover Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Iris images contain rich texture information for reliable personal identification. However, forged iris patterns may be used to spoof iris recognition systems. This paper proposes an iris anti-spoofing approach based on the texture discrimination between genuine and fake iris images. Four texture analysis methods include gray level co-occurrence matrix, statistical distribution of iris texture primitives, local binary patterns (LBP) and weighted-LBP are used for iris liveness detection. And a fake iris image database is constructed for performance evaluation of iris liveness detection methods. Fake iris images are captured from artificial eyeballs, textured contact lens and iris patterns printed on a paper, or synthesised from textured contact lens patterns. Experimental results demonstrate the effectiveness of the proposed texture analysis methods for iris liveness detection. And the learned statistical texture features based on weighted-LBP can achieve 99accuracy in classification of genuine and fake iris images.

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Authors and Affiliations

  1. Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing, 100190, P.R. China

    Zhenan Sun & Tieniu Tan

Authors
  1. Zhenan Sun
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  2. Tieniu Tan
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Corresponding author

Correspondence to Zhenan Sun .

Editor information

Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, United Kingdom

    Mark S. Nixon

  3. Institute of Automation Center for Biometrics and Security Resea, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

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© 2014 Springer-Verlag London

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Sun, Z., Tan, T. (2014). Iris Anti-spoofing. In: Marcel, S., Nixon, M., Li, S. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_6

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  • DOI: https://doi.org/10.1007/978-1-4471-6524-8_6

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6523-1

  • Online ISBN: 978-1-4471-6524-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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