Iris Boundary Localization Based on Improve Hough Transform

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Abstract:

ris location is one of the key steps of iris recognition system. Non-ideal iris image has some problems, such as eyelid and eyelash occlusion, low contrast of iris and sclera, uneven illumination, and so on. Because of that, its difficult to identify the boundary, especially the exterior boundary. Therefore, this paper proposes a method based on the improved Hough Transform. First, use the minimum method to find the datum point in the pupil, after that identify the valid area of the interior boundary base on that point. Apply the improved Hough Transform to that valid area to identify the interior boundary of the iris image. Then regard the center of the interior circle as our new datum point, use the same method to identify the exterior boundary. Experiment results show that our algorithm has higher accuracy than traditional method on the non-ideal iris image segmentation.

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Advanced Materials Research (Volumes 760-762)

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1576-1580

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] J. Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11): 1148-1161, Nov. (1993).

DOI: 10.1109/34.244676

Google Scholar

[2] J. Daugman, How Iris Recognition Works, IEEE Trans. Circuits and Systems for Video Technology, 14(1): 21-30, Jan. (2004).

DOI: 10.1109/tcsvt.2003.818350

Google Scholar

[3] John Daugman. Probing the uniqueness and randomness of iriscodes: Results from 200 billioniris pair comparisons. Proceedings of the IEEE, 2006, Vol. 94:1927-(1934).

DOI: 10.1109/jproc.2006.884092

Google Scholar

[4] R. Wildes, Iris Recognition: An Emerging Biometric Technology, Proc. IEEE, 85(9): 1348-1363, Sep. (1997).

DOI: 10.1109/5.628669

Google Scholar

[4] L. Ma, T. Tan, Y. Wang, D Zhang, Personal Identification Based on Iris Texture Analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, 25(12): 1519-1533, Dec. (2003).

DOI: 10.1109/tpami.2003.1251145

Google Scholar

[5] Z. Sun, Y. Wang, T. Tan, J. Cui, Improving Iris Recognition Accuracy via Cascaded Classifiers, IEEE Trans. System, Man and Cybernetics Part C: Applications and Reviews, 35(3): 435-441, Aug. (2005).

DOI: 10.1109/tsmcc.2005.848169

Google Scholar

[6] Zheng MA. Robust Iris Localization Algorithm., Journal of University of Electronic Science and Technology of China, 2010, 39(6): 920-923. (in Chinese).

Google Scholar

[7] Weiqi Yuan, Hao Wang. A Non-ideal I ris Localization Algorithm Based on Elliptical Projection(J),. Acta Electronica Sinica,2011, 4: 958-962. (in Chinese).

Google Scholar

[8] K Roy, P Bhattacharya, C Y Suen. Iris segmentation using variational level set method. Optics and Laser in Engineering, 2011, 49 (1): 578-588.

DOI: 10.1016/j.optlaseng.2010.09.011

Google Scholar

[9] H. L. Wan, M. Han, and T. Wang, Multiresolution Based Segmentation for Nonideal Iris with Nonlinear Diffusion, Automation and Robotics, D. Yang, Editor. 2012, Springer Berlin Heidelberg. pp.107-111.

DOI: 10.1007/978-3-642-25992-0_14

Google Scholar

[10] Qichuan Tian, Quan Pan, Yongmei Cheng, Hongcai Zhang. Study on Iris Boundary Localization Under Different Illumination,. Journal of Op to electronics·Laser, 2006, 17(4): 488-492. (in Chinese).

Google Scholar