A Robust Boundary Localization for Degraded Iris Images

Abstract:

Boundary localization is one of the key issues for reliable iris recognition system. For degraded iris images, some frequently-occurred cases such as dominant texture patterns, eyelashes or eyelids occlusions, low contrast between iris and sclera, and pupil deviation, will lead to inaccurate boundary localization. Specifically, if the intensive transition from iris to sclera is too smooth, outer boundary localization will be very difficult. To stress the problem, in this paper the boundary localization method is proposed in which nonlinear gray-level transformation is innovated in outer boundary localization process. The experimental results depict that our algorithm have improved the localization accuracy for degraded iris compared to the classical algorithms.

• Periodical: Advanced Materials Research (Volumes 846-847)
• Pages: 986-990
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.846-847.986
• Online since: November 2013
• Authors: Tian Ping Li*, Xiao Wei Wang
• Keywords: Biometrics, Boundary Localization, Image Enhancement, Iris Image Segmentation, Iris Recognition

References

[1] J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15 (11), 1993: 1148-1161.

DOI: 10.1109/34.244676

[2] J. Daugman. New methods in iris recognition. IEEE Trans. System, Man, and Sybernetics—Part B: Cybernetics, 37 (5), 2007: 1167-1175.

DOI: 10.1109/tsmcb.2007.903540

[3] R. PWildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9), 1997: 348-1363.

[4] Y. H. Wang, Y. Zhu, T. N. Tan. Biometrics personal identification based on iris pattern , Acta Automatic Sinica, 28(1): 2002: 1-10.

[5] K. Roy, P. Bhattacharya, C. Y. Suen CY. Iris segmentation using variational level set method. Optics and Lasers in Engineering, 49 (4), 2011: 578-588.

DOI: 10.1016/j.optlaseng.2010.09.011

[6] S. Pundlik, D. Woodard, S. Birchfield S. Iris segmentation in degraded images using graph cuts. Image Vision and Computing, 28 (12), 2010: 1671-1681.

DOI: 10.1016/j.imavis.2010.05.004

[7] A. Miguel. Luengo-Oroz, F. Emmanuel, J. Angulo. Robust iris segmentation on uncalibrated noisy images using mathematical morphology. Image Vision and Computing, 28 (2), 2010: 246-253.

DOI: 10.1016/j.imavis.2009.04.018

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

[9] Ju, Y.W., Tian, Z., Zhang, Y. (2006). Efficient image segmentation method based on resolution and region information fusion. Chinese Optics Letters, 4(11): 639～642.

[10] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. (2009). Digital Image Processing(3rd edition). Beijing: Publishing House of Electronics Industry, 1-609.

[11] Huang, J.L., Zheng, X.M. (2008). Improved image edge detection based on Canny operator. Computer and Applications, 44(25): 170-172.