Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, CIB 2009 - Proceedings

Volumn , Issue , 2009, Pages 46-51

  Broussard, Randy P ^a   Ryes, Robert W ^a  

^a UNITED STATES NAVAL ACADEMY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE CONTOURS; ARTIFICIAL NEURAL NETWORK; BINARY EDGE IMAGES; CO-LOCATED; FEATURE SALIENCY; FEATURE SETS; GRAY-SCALED IMAGES; IRIS RECOGNITION; IRIS SEGMENTATION; IRIS SEGMENTATION METHOD; SEGMENTATION ERROR; SEGMENTATION METHODS; SPECULAR REFLECTIONS;

BACKPROPAGATION; BIOMETRICS; BUFFER STORAGE; HOUGH TRANSFORMS; NEURAL NETWORKS; PIXELS;

EDGE DETECTION;

EID: 67650410446     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIB.2009.4925685     Document Type: Conference Paper

References (21)

1. J. Daugman, "Probing the Uniqueness and Randomness ofIrisCodes: Results From 200 Billion Iris Pair Comparisons", Proceedings of the IEEE, Vol. 94, No. 11, pages 1927-1935, November 2006. (Pubitemid 46445563)
2. Iris Challenge Evaluation (ICE) database, available at http://iris.nist.govlice/, accessed June 12,2008.
3. A. Ross, S. Shah, "Segmenting Non-Ideal Irises Using Geodesic Active Contours", Biometrics Consortium Coriference, 2006 Biometrics Symposium.
4. J. Zuo, N.D. Kalka, NA Schmid, "A Robust Iris Segmentation Procedure for Unconstrained Subject Presentation", Biometrics Consortium Coriference, 2006 Biometrics Symposium.
5. H. Proenca, L.A. Alexandre, "Iris segmentation methodology for noncooperative recognition", IEEE Proceeding on Vision, Image and Signal Processing, Volume 153, Issue2, Pages 199-205, April 2006.
6. Proenca, LA Alexandre, 'UBIRIS: a noisy iris image database', ICIAP 2005, 13th Int. Con! on Image AnalySiS and Processing, Cagliari, Italy, 6-8 September 2005, Lect. Notes Comput. Sci., 3617, pp. 970-977, ISBN 3-540-28869-4. httplliris.di.ubi.pt
7. J.G. Daugman, 'High confidence visual recognition of persons by a test of statistical independence', IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (11), pp. 1148-1161
8. RP. Wildes, 'Iris recognition: an emerging biometric technology', Proc. IEEE, 1997, 85, (9), pp. 1348-1363 (Pubitemid 127745626)
9. J. Cui, et. al., 'A fast and robust iris localization method based on texture segmentation', 2004. http://nJprwe b. ia. ac. cn/englishlirds/papersl cuij1/SPIE. pdf
10. J. Huang, Wang, T. Tan, and 1. Cui, 'A new iris segmentation method for recognition'. Proc. 17th Int. Con! on Pattern Recognition (ICPR04), Cambridge 2004, Vol. 3, pp. 554-557
11. L. Ma, Wang, and D. Zhang, 'Efficient iris recognition by characterizing key local variations', IEEE Transaction on Image Processing, 2004, 13, (6), pp. 739-750
12. L. Ma, T. Tan, Wang, and D. Zhang, 'Personal identification based on iris texture analysis', IEEE Trans. Pattern Anal. Mach. Intell., 2003,25, (12),pp. 1519-1533
13. L. Ma, Wang, and T. Tan, 'Iris recognition based on multichannel Gabor filtering'. ACCV2002: 5th Asian Corif. on Computer Vision, Melbourne, Australia, January 2002, Vol. I, pp. 279-283
14. W.K. Kong, and D. Zhang, 'Accurate iris segmentation method based on novel reflection and eyelash detection model' . Proc. 2001 Int. Symp. on Intelligent Multimedia, Video and Speech Processing, Hong Kong, May 200 I
15. Y. Du, et. aI., 'A new approach to iris pattern recognition'. SPIE European Symposium on Optics/Photonics in Defence and Security, London, UK, October 2004
16. J. Kim, S. Cho, and 1. Choi, 'Iris recognition using wavelet features', Journal on VLSI Signal Process., 2004, 38, (2), pp. 147-156
17. A.P. Dempster, N. Laird, and D. Rubin, 'Maximum likelihood from incomplete data via the EM algorithm', J. R Stat. Soc., 1977,39, pp. 1-38
18. L.R Kennell, RW. Ives, RM. Gaunt, "Binary morphology and local statistics applied to iris segmentation for recognition," Proceedings of the 13th Annual International Conference on Image Processing, Oct. 2006.
19. D.M. Momo, S. Rakshit, and D. Zhang, University of Bath, U.K. Iris Image Database, http://www.bath.ac.ukIe1ec-engipages/sipgjirisweb.
20. D.W. Ruck, et al., "The multilayer perceptron as an approximation to a Bayes optimal discriminant function", IEEE Transactions on Neural Networks, Vol 4, pp. 296-298, 1990
21. G. Cybenko, "Approximation by Superpositions ofSigmodial Functions", Mathematics of Control, Signals, and Systems, 1982