Skip to main content

Improved Techniques for an Iris Recognition System with High Performance

  • Conference paper
  • First Online:
AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

Included in the following conference series:

Abstract

We describe in this paper efficient techniques for iris recognition system with high performance from the practical point of view. These techniques range every step for an iris recognition system from the image acquisition step to the final step, the pattern matching, and contain as follows: a method of evaluating the quality of an image in the image acquisition step and excluding it from the subsequent processing if it is not appropriate, a bisection-based Hough transform method on the edge components for detecting the center of the pupil and localizing the iris area from an eye image, an elastic body model for transforming the localized iris area into a simple coordination system, and a compact and efficient feature extraction method which is based on 2D multiresolution wavelet transform. By exploiting these techniques, we can improve the system performance in terms of computationally efficient, and more accurate and robust against noises.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. John G. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(11), pp. 1148–1161, 1993.

    Article  Google Scholar 

  2. Wildes, R.P., “Iris Recognition: An Emerging Biometric Technology”, Proc. of the IEEE, 85(9), pp. 1348–1363, 1997.

    Google Scholar 

  3. Boles, W.W., Boashash, B., “A Human Identification Technique Using Images of the Iris and Wavelet Transform”, IEEE Trans. on Signal Processing, 46(4), pp. 1185–1188, 1998.

    Article  Google Scholar 

  4. Williams, G.O., “Iris Recognition Technology”, IEEE Aerospace and Electronics Systems Magazine, 12(4), pp. 23–29, 1997.

    Article  Google Scholar 

  5. Dimitrios Ioammou, Walter Huda, Andrew F. Laine, “Circle recognition through a 2D Hough Transform and radius histogramming”, Image and Vision Computing, 17, pp.15–26, 1999.

    Article  Google Scholar 

  6. Stephane. G. Mallet., “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, IEEE Trans. Pattern Recognition and Machine Intelligence, 11(4), pp.674–693, 1989.

    Article  Google Scholar 

  7. Stephane. G. Mallet., “Wavelet for a Vision”, Proceedings of the IEEE, 84(4), pp.604–614, 1996.

    Google Scholar 

  8. I. Daubechies., “Orthonormal bases of compactly supported wavelets”, Comm. Pure Appl. Math.,41, pp. 909–996, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  9. D. Randall Wilson, Tony R. Martinez, “Improved Heterogeneous Distance Functions”, Journal of Artificial Intelligence Research, 6, pp.1–34, 1997

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kee, G., Byun, Y., Lee, K., Lee, Y. (2001). Improved Techniques for an Iris Recognition System with High Performance. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_16

Download citation

  • DOI: https://doi.org/10.1007/3-540-45656-2_16

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics