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On Optimizing Feature Vectors from Efficient Iris Region Normalization for a Ubiquitous Computing

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Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3480))

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Abstract

Iris patterns are believed to be an important class of biometrics suitable for subject verification and identification applications. An efficient approach for iris recognition is presented in this paper. An efficient iris region normalization consists of a doubly polar coordinate and noise region exclude. And then a Haar wavelet transform is used to extract features from iris region of normalized. From this evaluation, we obtain iris code of small size and very high recognition rate. This effort is intended to enable a human authentication in small embedded systems, such as an integrated circuit card (smart cards).

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Author information

Authors and Affiliations

  1. Department of Information and Control Engineering, Kwangwoon University, 447-1, Wolgye-dong, Nowon-gu, Seoul, 139-701, Korea

    Bong Jo Joung

  2. Division of Information and Communication Eng., Seoul Women’s University, 126, Kongnung2-dong, Nowon-gu, Seoul, 139-774, Korea

    Woongjae Lee

Authors
  1. Bong Jo Joung
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  2. Woongjae Lee
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, P.O. Box, I-06123, Perugia, Italy

    Antonio Laganà

  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

    Heow Pueh Lee

  6. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  7. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  8. OptimaNumerics Ltd, P.O. Box, Belfast, United Kingdom

    Chih Jeng Kenneth Tan

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© 2005 Springer-Verlag Berlin Heidelberg

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Joung, B.J., Lee, W. (2005). On Optimizing Feature Vectors from Efficient Iris Region Normalization for a Ubiquitous Computing. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_126

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  • DOI: https://doi.org/10.1007/11424758_126

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25860-5

  • Online ISBN: 978-3-540-32043-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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