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Reseach Article

Iris Recognition based on Robust Features Matching

by R. M. Farouk
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 6
Year of Publication: 2012
Authors: R. M. Farouk
10.5120/6788-9101

R. M. Farouk . Iris Recognition based on Robust Features Matching. International Journal of Computer Applications. 45, 6 ( May 2012), 51-55. DOI=10.5120/6788-9101

@article{ 10.5120/6788-9101,
author = { R. M. Farouk },
title = { Iris Recognition based on Robust Features Matching },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 6 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 51-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number6/6788-9101/ },
doi = { 10.5120/6788-9101 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:55.812592+05:30
%A R. M. Farouk
%T Iris Recognition based on Robust Features Matching
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 6
%P 51-55
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we have proposed a new technique to select a certain point in a segmented iris so called regions of interest. A robust feature descriptor based on Gabor wavelet and Discrete Fourier Transform (DFT) is introduced. We have extracted the robust features around the inside selected iris points. The selected points are connected with each other to form a graph. This graph is afforded to handle even globally warped irises, by enhancing the robustness of node descriptors to a global warping, and introducing warping-compensated edges in graph matching cost function. The performance of the proposed approach is evaluated through the recognition simulation based on arbitrary irises. Recognition results are given for galleries of irises from CASIA and UBIRIS database. We also compare our results with previous work and we have found that, the proposed approach is an effective technique for iris matching process especially in case of noise iris.

References
  1. Jain A. , Bolle R. , and Kanti S. P. Biometrics: Personal Identification in a Networked Society. Kluwer, 1998.
  2. Boweyer, K. W. , Hollingsworth, K. , Patrick, J. F. , 2008. Image understanding for iris biometrics: A survey, Computer Vision and Image Understanding, 110, 281-307.
  3. Adler A. Physiology of Eye: Clinical Application. London, the C. V. Mosby Company, fourth edition, 1965.
  4. Daugman J. Biometric Personal Identification System Based on Iris Analysis. US Patent no. 5291560, 1994.
  5. Wildes, R. P. , Iris Recognition: An Emerging Biometric Technology, Proceedings of the IEEE, Vol. 85(9), 1348-1363, 1997.
  6. Daugman, J. , Demodulation by Complex-valued Wavelets for Stochastic Pattern Recognition, International Journal of Wavelets, Multiresolution and Information Processing, Vol. 1(1), 1-17, 2003.
  7. Ma, L. , Tan, T. , Wang, Y. , Zhang, D. , Efficient Iris Recognition by Characterizing Key Local Variations, IIEEE Transactions on Image Processing, 13, 739-750, 2003.
  8. Ma, L. , Tan, T. , Wang, Y. , Zhang, D. , 2004. Local intensity variation analysis for iris recognition, Pattern Recognition, 37, 1287–1298, 2004.
  9. Algrids B. , Justas K. and Volker K. , Iris recognition by fusing different representations of multi-scale Taylor expansion, Computer Vision and Image Understanding, 115, 6, (2011), 804-816.
  10. Pundlik S. , Woodard D. , and Birchfield S. , Iris segmentation in non-ideal images using graph cuts, Image and Vision Computing, 28, 12, (2010), 1671-1681.
  11. Rankin D. M. , Scotney B. W. , Morrow P. J. , and Pierscionek B. K. , Iris recognition failure over time: The effects of texture, Pattern Recognition, 45, 1, (2012), 145-150.
  12. Luo Z. , Iris feature extraction and recognition based on wavelet Based contourlet transform, Procedia Engineering, 29, (2012), 3578 – 3582.
  13. Farouk, R. M. , 2011. Iris recognition based on elastic graph matching and Gabor wavelet, Computer Vision and Image Understanding, 115, 1239–1244.
  14. Farouk, R. M. Analytical analysis of image representation by their discrete wavelet transforms, International Journal of Computer Science 3 (4), 216–221, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition Iris Segmentation Features Extraction Wavelets Robust Jet