CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Real Time Iris Image Segmentation for Non Co-Operative Environment

by Patil Mayur J., K. P. Adhiya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 14
Year of Publication: 2014
Authors: Patil Mayur J., K. P. Adhiya
10.5120/15273-3944

Patil Mayur J., K. P. Adhiya . Real Time Iris Image Segmentation for Non Co-Operative Environment. International Journal of Computer Applications. 87, 14 ( February 2014), 1-7. DOI=10.5120/15273-3944

@article{ 10.5120/15273-3944,
author = { Patil Mayur J., K. P. Adhiya },
title = { Real Time Iris Image Segmentation for Non Co-Operative Environment },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 14 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number14/15273-3944/ },
doi = { 10.5120/15273-3944 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:52.748679+05:30
%A Patil Mayur J.
%A K. P. Adhiya
%T Real Time Iris Image Segmentation for Non Co-Operative Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 14
%P 1-7
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the area of iris recognition, the most difficult & complex part is iris segmentation. There are several different algorithms proposed & developed in recognition area. Iris recognition becomes more difficult in case of non co-operative environment. Most of the time due to some condition or situation an eye changes with respect to lens of camera, size, shape & iris pattern details will change as well as it is very difficult to match these samples with enrolled images using traditional method. The traditional algorithm can suitable for frontal iris images. In non co-operative environment there is no consistent location of eye in the image. Sometimes, the eye may have closed or blinking without a proper iris pattern or there is no eye in the image. When such situation occurs, the result of traditional iris image recognition method would dramatically decrease. So the real time image processing provides better information as compare to traditional method & it is proper solution as compare to processing a single image.

References
  1. Biometric history. August 2006.
  2. Iris recognition. August 2006.
  3. O. A. Abbas. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology, 5(3):320–325, July 2008.
  4. B. O. Adegoke, E. O. Omidiora, S. A. Falohun, and J. A. Ojo. Iris segmentation: a survey. International Journal of Modern Engineering Research (IJMER), 3(4):1885–1889, Jul - Aug. 2013.
  5. T. Camus and R Wildes. Reliable & fast eye finding in closeup images. IEEE 16th International Conference on Pattern Recognition, Quebec, Canada, pages 389–394, 2004.
  6. J. Daugman. High confidence visual recognition of person by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. , 15(11):1148–1161, 1993.
  7. Y. Du, R. Ives, D. Etter, T. Welch, and C. Chang. A new approach to iris pattern recognition. SPIE Europian Symp. on Optics/Photonic in Defence and Security, London, UK, October 2004.
  8. L. Flom and A. Safir. Iris recognition system. U. S. Patent 4641394, 1987.
  9. K. Gulmire and S. Ganorkar. Iris recognition using independent component analysis. International Journal of Emerging Technology and Advanced Engineering, 2(7):433–437, July 2012.
  10. A. Hyvarinen and E. Oja. Independent component analysis: Algorithms and applications. Neural Networks, 13(4):411– 430, 2000.
  11. I. T. Jolliffe. Principal Component Analysis,. Springer, 2 edition, April 2002.
  12. Anil K. Jain and Ajay Kumar. Biometrics of next generation: An overview. Second Generation Biometrics, Springer, 2010.
  13. G. Komarasamy and A. Wahi. An optimized k-means clustering technique using bat algorithm. European Journal of Scientific Research, 84(2):263–273, 2012.
  14. L. Liam, A. Chekima, L. Fan, and J. Dargham. Iris recognition using self orgnizing neural network. IEEE 2002 Students Conference on Research & Developing System, Malaysia, pages 169–172, 2002.
  15. G. R. Naik and D. K. Kumar. An overview of independent component analysis and its applications. Informatica, 35:63– 81, 2011.
  16. H. Proenca and L. A. Alexandre. Iris segmentation methodology for non co-operative recognition. IEE Proceedings Vision, Image & Signal Processing, 153(2):199–205, April 2006.
  17. Hugo Pedro Martins Carrico Proenca. Towards Non Cooperative Biometric Iris Recognition. PhD thesis, Department of Computer Science, University of Beira Interior, Covilha, Portugal, October 2006.
  18. Martin Roche, C. Sanchez Avila, and R. Sanchez Reillo. Iris recognition for biometric identification using dyadic wavelet transform zero crossing. IEEE Aerosp. Electronics System Mag. , 17(10):3–6, 2002.
  19. Dhananjay Theckedath. Iris detection based on principal component analysis-eigen irises. Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India, 1:49– 52, 2007-2008.
  20. T. Velmurugan and T. Santhanam. Performance evaluation of k-means and fuzzy c-means clustering algorithms for statistical distributions of input data points. European Journal of Scientific Research, 46(3):320–330, 2010.
  21. R. Wildes. Iris recognition: An emerging biometric technology. IEEE Proceding, 85(9):1348–1363, 1997. 7
Index Terms

Computer Science
Information Sciences

Keywords

K-Mean PCA ICA Iris Pattern Matching

Powered by PhDFocusTM