Paper title:

Investigation on Optimization in Segmentation Phase of Iris Recognition

Published in: Issue 2, (Vol. 4) / 2010
Publishing date: 2010-04-30
Pages: 41-44
Author(s): SHANMUGAM Selvamuthukumaran, NAGARAJAN Malmurugan
Abstract. In a progressively more digital society, the demand for secure identification has led to amplified development of biometric systems. Iris biometric systems are becoming widely adopted and accepted as one of the most effective ways to positively identify people. In this paper, the Segmentation phases of Iris recognition has been examined. The performance of the Segmentation phase could be amplified by the proposed optimization technique- Optimized Iris Segmentation using Sobel Edge Detection. By the proposed method, the overall rank-one recognition rate of 90% is being achieved which is much better than reported accuracies for iris recognition in the literature. Also the proposed approach makes the overall iris recognition system performance by the improvement factor of 10 fold as well.
Keywords: Iris, Security, Optimization, Segmentation, Edge Detection, Sobel Operator

1.. J. G. Daugman, Ph.D., “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 15, No. 11, 1993.

2.. J.G.Daugman, “How iris recognition works”. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):Pp:21–30, 2004.

3.. J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, and W. Y. Zhao. “Iris on the Move TM : Acquisition of images for iris recognition in less constrained environments”. Proceedings of the IEEE, 94(11):1936–1946, 2006.

4.. Xin Li., “Modeling intra-class variation for non-ideal iris recognition”, In Springer LNCS 3832: Int. Conf. on Biometrics, Pp: 419–427, 2006.

5.. J.G.Daugman, “New methods in iris recognition”, IEEE Transactions on Systems, Man and Cybernetics - B, 37(5):Pp:1167–1175, 2007.

6.. Jason Thornton, Marios Savvides, and B.V.K. Vijaya Kumar, “An evaluation of iris pattern representations”, In Biometrics: Theory, Applications, and Systems, 2007.

7.. L. Masek, “Recognition of Human Iris Patterns for Biometric Identification. jects/libor/”,The University of Western Australia, 2003

8.. Xiaomei Liu, Kevin W. Bowyer, Patrick J. Flynn: “Experiments with an Improved Iris Segmentation Algorithm”, Fourth IEEE Workshop on Automatic Identification Advanced Technologies(AutoID 2005), Pp:118-123, 2005

9.. H. Proenca and L. A. Alexandre. “Iris segmentation methodology for non-cooperative recognition”, IEEE Proc.-Vision, Image and Signal Processing, 153(2):Pp:199–205, 2006.

10.. Jinyu Zuo, Nalini K. Ratha and Jonathan H. Connell, “ A New Approach for Iris Segmentation”, Computer Vision and Pattern Recognition Workshops, Pp:1-6, 2008.

11.. H. Proenc¸a and L. A. Alexandre. Iris segmentation methodology for non-cooperative recognition. IEE Proc.-Vision, Image and Signal Processing, 153(2):199–205, 2006.

12.. Jinyu Zuo, Nalini K. Ratha and Jonathan H. Connell. A New Approach for Iris Segmentation, Computer Vision and Pattern Recognition Workshops, 1-6, 2008.

13.. Chinese Academy of Sciences – Institute of Automation, Database of Eye Images.

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