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Journal of Electrical and Computer Engineering
Volume 2016, Article ID 4709876, 10 pages
http://dx.doi.org/10.1155/2016/4709876
Research Article

A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani 333031, India

Received 22 August 2015; Accepted 11 May 2016

Academic Editor: William Sandham

Copyright © 2016 Vineet Kumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993. View at Publisher · View at Google Scholar · View at Scopus
  2. R. P. Wildes, “Iris recognition: an emerging biometrie technology,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1348–1363, 1997. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519–1533, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: a survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281–307, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. CASIA Iris Image Database, 2010, http://biometrics.idealtest.org/.
  7. F. Jan, I. Usman, S. A. Khan, and S. A. Malik, “A dynamic non-circular iris localization technique for non-ideal data,” Computers and Electrical Engineering, vol. 40, no. 8, pp. 215–226, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Wang, Q. Li, A. A. Abd El-Latif, T. Zhang, and X. Niu, “Toward accurate localization and high recognition performance for noisy iris images,” Multimedia Tools and Applications, vol. 71, no. 3, pp. 1411–1430, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Radman, K. Jumari, and N. Zainal, “Fast and reliable iris segmentation algorithm,” IET Image Processing, vol. 7, no. 1, pp. 42–49, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Shah and A. Ross, “Iris segmentation using geodesic active contours,” IEEE Transactions on Information Forensics and Security, vol. 4, no. 4, pp. 824–836, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Jan, I. Usman, and S. Agha, “Iris localization in frontal eye images for less constrained iris recognition systems,” Digital Signal Processing, vol. 22, no. 6, pp. 971–986, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. J. Zuo and N. A. Schmid, “On a methodology for robust segmentation of nonideal iris images,” IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 40, no. 3, pp. 703–718, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Jan, I. Usman, and S. Agha, “Reliable iris localization using Hough transform, histogram-bisection, and eccentricity,” Signal Processing, vol. 93, no. 1, pp. 230–241, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. S. A. Sahmoud and I. S. Abuhaiba, “Efficient iris segmentation method in unconstrained environments,” Pattern Recognition, vol. 46, no. 12, pp. 3174–3185, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Proença, “Iris recognition: on the segmentation of degraded images acquired in the visible wavelength,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1502–1516, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Li, X. Liu, L. Xiao, and Q. Song, “Robust and accurate iris segmentation in very noisy iris images,” Image and Vision Computing, vol. 28, no. 2, pp. 246–253, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Khalighi, F. Pak, P. Tirdad, and U. Nunes, “Iris recognition using robust localization and nonsubsampled contourlet based features,” Journal of Signal Processing Systems, vol. 81, no. 1, pp. 111–128, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. K. M. I. Hasan and M. A. Amin, “Dual iris matching for biometric identification,” Signal, Image and Video Processing, vol. 8, no. 8, pp. 1605–1611, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Marciniak, A. Dąbrowski, A. Chmielewska, and A. A. Krzykowska, “Selection of parameters in iris recognition system,” Multimedia Tools and Applications, vol. 68, no. 1, pp. 193–208, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Pan, W.-S. Chu, J. M. Saragih, F. D. La Torre, and M. Xie, “Fast and robust circular object detection with probabilistic pairwise voting,” IEEE Signal Processing Letters, vol. 18, no. 11, pp. 639–642, 2011. View at Publisher · View at Google Scholar
  21. J. Cauchie, V. Fiolet, and D. Villers, “Optimization of an Hough transform algorithm for the search of a center,” Pattern Recognition, vol. 41, no. 2, pp. 567–574, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. A. Bendale, A. Nigam, S. Prakash, and P. Gupta, “Iris segmentation using improved Hough transform,” in Emerging Intelligent Computing Technology and Applications, D.-S. Huang, P. Gupta, X. Zhang, and P. Premaratne, Eds., vol. 304 of Communications in Computer and Information Science, pp. 408–415, 2012. View at Publisher · View at Google Scholar
  23. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, “Digital image processing using Matlab—Gonzalez Woods & Eddins.pdf,” Education, vol. 624, no. 2, p. 609, 2004. View at Google Scholar
  24. E. R. Davies, Computer and Machine Vision: Theory, Algorithms, Practicalities, Academic Press, New York, NY, USA, 2012.
  25. P. Soille, Morphological Image Analysis: Principles and Applications, Springer, Berlin, Germany, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  26. S. J. K. Pedersen, “Circular Hough transform,” in Vision, Graphics and Interactive Systems, Aalborg University, 2007. View at Google Scholar