Segmentation Techniques for Iris Recognition System
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Full Text(PDF, 3000) PP.
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Author(s) |
Surjeet Singh, Kulbir Singh |
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KEYWORDS |
Active contour, Biometrics, Daugman’s method, Hough Transform, Iris, Level Set method, Segmentation.
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ABSTRACT |
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Iris recognition systems capture an image of an individual's eye, the iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. This paper discusses the performance of segmentation techniques for iris recognition systems to increase the overall accuracy.
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References |
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[1] V. Matyas and Z. Riha, “Toward reliable user
authentication through biometrics,” IEEE Security and
Privacy, vol. 1, no. 3, pp. 45–49, 2003.
[2] J. G. Daugman, “Phenotypic versus genotypic approaches
to face recognition,” Face Recognition: From Theory to
Applications, pp. 108–123. Heidelberg: Springer-Verlag,
1998.
[3] S. D. Fried, “Domain access control systems and
methodology,”http://www.itu.dk/courses/SIAS/E2005/AU22
40 01.pdf , 2004.
[4] M. Bromba, Biometrics FAQ’s,
http://www.bromba.com/faq/biofaqe.htm, 2010.
[5] A. K. Jain, R. Bolle, and S. Pankanti, Personal
Identification in networked society, 2nd edition. Kluwer
Academic Publisher, E.U.A., 1999.
[6] A. K. Jain, A.Ross, and S. Prabhakar, “An introduction to
biometric recognition,” IEEE Transactions on Circuits and
Systems for Video Technology, vol. 14, no. 1, pp. 4–19,
January 2004.
[7] S. Liu and M. Silverman, “A practical guide to biometric
security technology,” IT Professional, vol. 3, no 1, pp. 27–
32, January 2001.
[8] Biometrics and the courts,
http://ctl.ncsc.dni.us/biomet%20web/BMIndex.html,
2010.
[9] Idesia’s Biometric Technologies. Biometric comparison
table,http://www.idesiabiometrics.
com/technology/biometric_comparison_table.
html, 2010.
[10] International Biometric Group, “Which is the best
biometric technology?,”
http://www.biometricgroup.com/reports/public/report
s/best_biometric.html, 2010.
[11] J. D. Woodward, K. W. Webb, E. M. Newton, M. A.
Bradley, D. Rubenson, K. Larson, J. Lilly, K. Smythe, B.
Houghton, H. A. Pincus, J. Schachter, and P. Steinberg,
“Army Biometric Applications - Identifying and
Addressing Socio-Cultural Concerns,” Rand Corporation,
Santa Monica, 2001.
[12] A. K. Khurana, Comprehensive Ophthalmology, New
Age International (P) Ltd., 4th edition, 2007.
[13] L. A. Remington, Clinical Anatomy of the Visual System,
Elsevier Inc., 2nd edition, 2005.
[14] J. G. Daugman, “High confidence visual recognition of
persons by a test of statistical independence,” IEEE
Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, no. 11, pp. 1148–1161, November
1993.
[15] K. Nishino and S. K. Nayar, “Eyes for relighting,” ACM
Trans. Graph., vol 23, no. 3, pp. 704–711, 2004.
[16] T.A. Camus and R. Wildes, “Reliable and fast eye finding
in close-up images,” Proceedings of the IEEE 16th International Conference on Pattern Recognition, pp. 389–
394, Quebec, August 2002.
[17] D. Martin-Roche, C. Sanchez-Avila, and R. Sanchez-
Reillo, “Iris recognition for biometric identification using
dyadic wavelet transform zero-crossing,” IEEE Aerospace
and Electronic Systems Magazine, Mag. 17, no. 10, pp. 3–
6, 2002.
[18] R. P. Wildes, “Iris recognition: an emerging biometric
technology,” Proceedings of the IEEE, vol. 85, no.9, pp.
1348–1363, U.S.A., September 1997.
[19] J. Cui, Y. Wang, T. Tan, L. Ma, and Z. Sun, “A fast and
robust iris localization method based on texture
segmentation,” Proceedings of the SPIE Defense and
Security Symposium, vol. 5404, pp. 401–408, August 2004.
[20] J. Huang, Y. Wang, T. Tan, and J. Cui, “A new iris
segmentation method for recognition,” Proceedings of the
17th International Conference on Pattern Recognition
(ICPR), vol. 3, pp. 23–26, 2004.
[21] W. K. Kong and D. Zhang, “Accurate iris segmentation
method based on novel reflection and eyelash detection
model,” Proceedings of the International Symposium on
Intelligent Multimedia, Video and Speech Processing, pp.
263–266, Hong Kong, May 2001.
[22] L. Ma, Y. Wang, and T. Tan, “Iris recognition using
circular symmetric filters,” Proceedings of the 25th
International Conference on Pattern Recognition, vol. 2,
pp. 414–417, Quebec, August 2002.
[23] 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. 2519–2533, December
2003.
[24] L. Ma, Y. Wang, and D. Zhang, “Efficient iris recognition
by characterizing key local variations,” IEEE Transactions
on Image Processing, vol. 13, no. 6, pp. 739–750, June
2004.
[25] L. Liam, A. Chekima, L. Fan, and J. Dargham, “Iris
recognition using self organizing neural network,”
Proceedings of the IEEE Student Conference on Research
and Developing Systems, pp. 169–172, Malasya, June
2002.
[26] Y. Du, R. Ives, D. Etter, T. Welch, and C. Chang, “A new
approach to iris pattern recognition,” Proceedings of the
SPIE European Symposium on Optics/Photonics in
Defence and Security, vol. 5612, pp. 104–116, October
2004.
[27] J. Mira and J. Mayer, “Image feature extraction for
application of biometric identification of iris - a
morphological approach,” Proceedings of the 16th
Brazilian Symposium on Computer Graphics and Image
Processing, pp. 391–398, Brazil, October 2003.
[28] J. Kim, S. Cho, and J. Choi, “Iris recognition using wavelet
features,” Kluwer Academic Publishers, Journal of VLSI
Signal Processing, no. 38, pp. 147– 256, November 2004.
[29] A. P. Dempster, N. Laird, and D. Rubin, “Maximum
likelyhood from incomplete data via the EM algorithm,”
Journal of the Royal Statistic Society, vol. 39, pp. 1–38,
1977.
[30] C. Tisse, L. Martin, L. Torres, and M. Robert, “Person
identification technique using human iris recognition,”
Proceedings of the 25th International Conference on
Vision Interface, pp. 294–299, Calgary, July 2002.
[31] C. Li, C. Xu, C. Gui, and M. D. Fox, “Level Set Evolution
Without Re-initialization: A New Variational
Formulation,” IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, vol. 1, pp. 430
– 436, 2005.
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