CFP last date
20 March 2024
Reseach Article

IRIS Pattern Recognition using Self-Organizing Neural Networks

Published on May 2012 by Savita Sondhi, Sharda Vashisth, Asha Gaikwad, Anjali Garg
National Conference on Advancement in Electronics & Telecommunication Engineering
Foundation of Computer Science USA
NCAETE - Number 1
May 2012
Authors: Savita Sondhi, Sharda Vashisth, Asha Gaikwad, Anjali Garg
2dc6daf9-fb3b-4413-ad45-58e8b986f701

Savita Sondhi, Sharda Vashisth, Asha Gaikwad, Anjali Garg . IRIS Pattern Recognition using Self-Organizing Neural Networks. National Conference on Advancement in Electronics & Telecommunication Engineering. NCAETE, 1 (May 2012), 12-17.

@article{
author = { Savita Sondhi, Sharda Vashisth, Asha Gaikwad, Anjali Garg },
title = { IRIS Pattern Recognition using Self-Organizing Neural Networks },
journal = { National Conference on Advancement in Electronics & Telecommunication Engineering },
issue_date = { May 2012 },
volume = { NCAETE },
number = { 1 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 12-17 },
numpages = 6,
url = { /proceedings/ncaete/number1/6589-1079/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement in Electronics & Telecommunication Engineering
%A Savita Sondhi
%A Sharda Vashisth
%A Asha Gaikwad
%A Anjali Garg
%T IRIS Pattern Recognition using Self-Organizing Neural Networks
%J National Conference on Advancement in Electronics & Telecommunication Engineering
%@ 0975-8887
%V NCAETE
%N 1
%P 12-17
%D 2012
%I International Journal of Computer Applications
Abstract

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach is receiving interest in both research and practical applications. Iris is a kind of physiological biometric feature. It contains unique texture and is complex enough to be used as a biometric signature. Compared with other biometric features such as face and fingerprint, iris patterns are more stable and reliable. This paper describes an iris recognition system, composed of iris image acquisition, iris image preprocessing, neural network training process and pattern matching. In this paper a digitally captured iris image is acquired and is then preprocessed. This is needed to remove the unwanted parts that are usually captured along with the iris image, to prevent effects due to a change in camera-to-face distance and also due to non-uniform illumination. The image thus obtained is trained using self organizing map (SOM) and finally decision is made by matching.

References
  1. John Daugman, "How Iris Recognition Works", IEEE Trans. On Circuits and Systems for Video Technology, Vol. 14, No. 1, pp 21-30, Jan 2004.
  2. John Daugman, Cathryn Downing"Epigenetic Randomness, Complexity and Singularity of Human iris Pattern", Proceedings of The Royal Society, pp 1737-1740, April 2001.
  3. John Daugman, "Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition", International Journal of Wavelets, Multiresolution and Information Processing, Vol. 1, No. 1, pp 1-17, Jan 2003.
  4. John Daugman, "Probing the Uniqueness and randomness of Iris Codes: Results from 200 Billion Iris Pair Comparisons", Proceedings of the IEEE, Vol. 94, No. 11, pp 1927-1935, Nov 2006.
  5. John Daugman, "New Methods in Iris Recognition", IEEE Trans. On Systems, Man and Cybernetics-Part B: Cybernetics, Vol. 13, No. 5, pp 1167-1175, Oct 2007.
  6. John Daugman, "The Importance of Being Random: Stastical Principles of iris Recognition", The Journal of Pattern Recognition Society, Vol. 36, pp 279-291, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Iris Pattern Recognition Self-organizing Neural Networks