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Improving Iris Recognition Accuracy via Cascaded Classifiers

  • Conference paper
Biometric Authentication (ICBA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3072))

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Abstract

As a reliable approach to human identification, iris recognition has received increasing attention in recent years. In the literature of iris recognition, local feature of image details has been verified as an efficient iris signature. But measurements from minutiae are easily affected by noises, which greatly limits the system’s accuracy. When the matching score between two intra-class iris images is near the local feature based classifier’s (LFC) decision boundary, the poor quality iris images are usually involved in matching. Then a novel iris blob matching algorithm is resorted to make the recognition decision which is more robust than the LFC in the noisy environment. The extensive experimental results demonstrate that the cascading scheme significantly outperforms individual classifier in terms of accuracy and robustness.

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Author information

Authors and Affiliations

  1. Center for Biometrics Authentication and Testing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing, 100080, P.R. China

    Zhenan Sun, Yunhong Wang, Tieniu Tan & Jiali Cui

Authors
  1. Zhenan Sun
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  2. Yunhong Wang
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  3. Tieniu Tan
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  4. Jiali Cui
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Editor information

Editors and Affiliations

  1. Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    David Zhang

  2. Department of Computer Science and Engineering, Michigan State University,  

    Anil K. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Sun, Z., Wang, Y., Tan, T., Cui, J. (2004). Improving Iris Recognition Accuracy via Cascaded Classifiers. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_58

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  • DOI: https://doi.org/10.1007/978-3-540-25948-0_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22146-3

  • Online ISBN: 978-3-540-25948-0

  • eBook Packages: Springer Book Archive

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