OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 50, Iss. 19 — Jul. 1, 2011
  • pp: 3191–3200

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Afshin Ghanizadeh, Amir Atapour Abarghouei, Saman Sinaie, Puteh Saad, and Siti Mariyam Shamsuddin  »View Author Affiliations


Applied Optics, Vol. 50, Issue 19, pp. 3191-3200 (2011)
http://dx.doi.org/10.1364/AO.50.003191


View Full Text Article

Enhanced HTML    Acrobat PDF (1616 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

© 2011 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.4995) Image processing : Pattern recognition, metrics

ToC Category:
Image Processing

History
Original Manuscript: July 26, 2010
Revised Manuscript: December 1, 2010
Manuscript Accepted: January 12, 2011
Published: June 23, 2011

Citation
Afshin Ghanizadeh, Amir Atapour Abarghouei, Saman Sinaie, Puteh Saad, and Siti Mariyam Shamsuddin, "Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata," Appl. Opt. 50, 3191-3200 (2011)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-50-19-3191


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society (Kluwer, 1999).
  2. S. Pankanti, R. M. Bolle, and A. Jain, “Biometrics: the future of identification,” Computer 33, 46–49 (2000).
  3. C. W. Oyster, The Human Eye Structure and Function(Sinauer, 1999).
  4. J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst.Video Technol. 14, 21–30 (2004). [CrossRef]
  5. J. G. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recogn. 36, 279–291(2003). [CrossRef]
  6. D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput. 28, 254–260 (2010). [CrossRef]
  7. J. Huang, X. You, Y. Y. Tang, L. Du, and Y. Yuan, “A novel iris segmentation using radial-suppression edge detection,” Signal Process. 89, 2630–2643 (2009). [CrossRef]
  8. A. Bertillon, “La couleur de l’iris,” Rev. Sci. Instrum. 36, 65–73 (1885).
  9. L. Flom and A. Safir, “Iris recognition system,” U.S. patent 4,641,349 (3 February 1987).
  10. R. Johnston, “Can iris patterns be used to identify people?” Annual Rep. LA-12331-PR (Los Alamos National Laboratory, Chemical and Laser Sciences Division, 1992), pp. 81–86.
  11. J. G. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” Int. J. Comput. Vis. 45, 25–38 (2001). [CrossRef]
  12. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993). [CrossRef]
  13. R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE 85, 1348–1363 (1997). [CrossRef]
  14. Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35, 435–441 (2005). [CrossRef]
  15. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process. 46, 1185–1188 (1998). [CrossRef]
  16. L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1519–1533 (2003). [CrossRef]
  17. J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698(1986). [CrossRef] [PubMed]
  18. S. Ulam, “Some ideas and prospects in biomathematics,” Annu. Rev. Biophys. Bioeng. 1, 277–292 (1972). [CrossRef] [PubMed]
  19. J. von Neumann, “The general and logical theory of automata,” in Cerebral Mechanisms in Behavior – The Hixon Symposium, L.A.Jeffress, ed. (Wiley, 1951), pp. 1–31.
  20. S. Amoroso and G. Cooper, “Tessellation structures for reproduction of arbitrary patterns,” J. Comput. Syst. Sci. 5, 455–464 (1971). [CrossRef]
  21. L. A. Zadeh, “Fuzzy sets,” Inf. Control 8, 338–353(1965). [CrossRef]
  22. H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Interval-valued fuzzy sets constructed from matrices: application to edge detection,” Fuzzy Sets Syst. 160, 1819–1840(2009). [CrossRef]
  23. F. Jacquey, F. Comby, and O. Strauss, “Fuzzy edge detection for omnidirectional images,” Fuzzy Sets Syst. 159, 1991–2010(2008). [CrossRef]
  24. H. R. Tizhoosh, “Fast fuzzy edge detection,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (IEEE, 2002), pp. 239–242.
  25. H. Beigy and M. R. Meybodi, “Open synchronous cellular learning automata,” Adv. Complex Syst. 10, 527–556(2007). [CrossRef]
  26. Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp.
  27. H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in 13th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science (Springer, 2005), pp. 970–977.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited