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Iris Localization using Daugman’s Intero-Differential Operator

by R. B. Dubey, Abhimanyu Madan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 3
Year of Publication: 2014
Authors: R. B. Dubey, Abhimanyu Madan
10.5120/16193-5433

R. B. Dubey, Abhimanyu Madan . Iris Localization using Daugman’s Intero-Differential Operator. International Journal of Computer Applications. 93, 3 ( May 2014), 6-12. DOI=10.5120/16193-5433

@article{ 10.5120/16193-5433,
author = { R. B. Dubey, Abhimanyu Madan },
title = { Iris Localization using Daugman’s Intero-Differential Operator },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 3 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number3/16193-5433/ },
doi = { 10.5120/16193-5433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:14:50.414389+05:30
%A R. B. Dubey
%A Abhimanyu Madan
%T Iris Localization using Daugman’s Intero-Differential Operator
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 3
%P 6-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is regarded as the most reliable and accurate biometric identification system. Most commercial iris recognition systems use patented algorithms developed by Daugman and these algorithms are able to produce perfect recognition rates. These algorithms are based on linear search methods which make the identification process extremely slow and also raise the false acceptance rate beyond the acceptable range. The proposed iris recognition approach consists of an automatic segmentation system that is based on the various algorithms and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes and reflections. Our proposed method has shown out performing results than existing Houghman algorithms.

References
  1. S. Dey, and D. Samanta, J. Daugman, "How iris recognition works," IEEE Trans on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21– 30, 2004.
  2. Iris Recognition [Online] Available: http://en. wikipedia. org/wiki/Iris_recognition
  3. E. Wolff. Antomy of the eye and orbit. 7th edition, H. K. Lewis and Ltd.
  4. en. wikipedia. org/wiki/Iris_ (anatomy)
  5. S. Dey, and D. Samanta, , " Iris data indexing method using Gabor energy features" IEEE Trans on information forensics and security, vol. 7, no. 4, August 2012.
  7. Casia-IrisV3-Interval Iris Image Database [Online]. Available: http:// www. cbsr. ia. ac. cn/IrisDatabase. htm
  8. J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. Pattern Anal. Mach. Intell. , ol. 15, no. 11, pp. 1148–1161, Nov. 1993.
  9. J. G. Daugman, "How iris recognition works", IEEE Trans Circuits Syst Video Technol, vol. 14, pp. 1–17, 2003.
  10. J. G. Daugman, "The importance of being random: statistical principles of iris recognition", Pattern Recognition, vol. 36, pp. 279–91, 2003.
  11. K. Roy, P. Bhattacharya, and C. Y. Suena, "Iris segmentation using variational level set method", Optics and Lasers in Engineering, vol. 49, pp. 578–588, 2011.
  12. A. Harjoko, S. Hartati, and H. Dwiyasa, " A Method for Iris Recognition Based on 1D Coiflet Wavelet", World Academy of Science, Engineering and Technology 56, 2009.
  13. R. T. Al-Zubi and D. I. Abu-Al-Nadi, "Automated personal Identification System Based on Human Iris Analysis", Pattern Analysis Application, vol. 10: pp. 147–164, 2007.
  14. L. Berggren, "Iridology: A critical review", Acta Ophthalmoligica, vol. 63, pp. 1–8, 1985.
  15. R. S. Worrall, "Iridology: Diagnosis or delusion", The Skeptical Inquirer, pp. 23–35, Spring 1983.
  16. R. A. Ramlee and S. Ranjit, "Using Iris Recognition Algorithm, Detecting Cholesterol Presence", 2009 International Conference on Information Management and Engineering.
  17. C. H. Daouk et al. , "Iris Recognition", Proceedings of the 2nd IEEE. International. Symposium on Signal Processing and Information Technology, pp. 558-562, 2002.
  18. L. Masek, 2003, Recognition of Human Iris Patterns for Biometric Identification, Thesis School of Computer Science and Software Engineering, University of Western Australia.
  19. Q. C. Tian, Q. Pan, Y. M. Cheng, Q. X. Gao, "Fast Algorithm and Application of Hough Transform in Iris Segmentation", Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on , vol. 7, no. , pp. 3977- 3980, 26-29 Aug. 2004.
  20. A. E. Yahya, M. J. Nordin, "A New Technique for Iris Localization", International Scientific Conference Computer Science'2008, Information Technology Journal, vol. 7(6):pp. 924-929, 2008.
  21. N. A. Jalil, R. Sahak and A. Saparon, "Iris localization using color segmentation and Circular Hough Transform", IEEE EMBS International Conference on Biomedical Engineering and Sciences pp. 784-788, 2012.
  22. J. Cui, Y. Wang, T. Tan, L. Ma, Z. Sun, "A Fast and Robust Iris Localization Method Based on Texture Segmentation", Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II, Pages 162 – 169.
  23. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride," A system for automated iris recognition" proceeding IEEE workshop on Apllication of Computer Vision, Sarasota, FL, pp. 121-128, 1994.
  24. W. Kong, D. Zhang, "Accurate iris segmentation based on novel reflection and eyelash detection model", Proc. 2001 Intl Symp Intelligent Multimedia, Video and Speech Proc. , 2001.
  25. C. Tisse, L. Martin, L. Torres and M. Robert, "Person identification technique using human iris recognition", Intl Conf. on Vision Interface, 2002.
  26. L. Ma, Y. Wang and T. Tan, "Iris recognition using circular symmetric filters", National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 2002.
  27. N. Ritter, "Location of the pupil-iris border in slit-lamp images of the cornea", Proc. Intl Conf. on the Image Analysis and processing, 1999.
  28. J. Daughman," New Methods in iris recognition," Cybernetics, vol. 37, no. 6, October 2007.
  29. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
  30. Samir Shah and Arun Ross,, "Iris segmentation using geodesic active contours", IEEE Trans on Information Forensics and Security, vol. 4, no. 4, December 2009.
  31. http://people. csse. uwa. edu. au/pk/studentprojects/libor/LiborMasekThesis. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition Daugman's Intero-differential operator Pupil Boundary Iris Boundary Segmentation.

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