Abstract
Iris segmentation plays the most important role in iris biometric system and it determines the subsequent recognizing result. So far, there are still many challenges in this research filed. This paper proposes a robust iris segmentation algorithm using active contours without edges and improved circular Hough transform. Firstly, we adopt a simple linear interpolation model to remove the specular reflections. Secondly, we combine HOG features and Adaboost cascade detector to extract the region of interest from the original iris image. Thirdly, the active contours without edges model and the improved circular Hough transform model are used for the pupillary and limbic boundaries localization, respectively. Lastly, two iris databases CASIA-IrisV1 and CASIA-IrisV4-Lamp were adopted to prove the efficacy of the proposed method. The experimental results show that the performance of proposed method is effective and robust.
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References
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993)
Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85, 1348–1363 (1997)
Shamsi, M., Saad, P.B., Ibrahim, S.B., Kenari, A.R.: Fast algorithm for iris localization using daugman circular integro differential operator. In: International Conference of Soft Computing and Pattern Recognition, SOCPAR 2009, pp. 393–398 (2009)
Chunping, W.: Research on iris image recognition algorithm based on improved differential operator. J. Convergence Inf. Technol. 8, 563–570 (2013)
Peihua, L., Xiaomin, L.: An incremental method for accurate iris segmentation. In: 19th International Conference on Pattern Recognition, 2008, ICPR 2008, pp. 1–4 (2008)
Bendale, A., Nigam, A., Prakash, S., Gupta, P.: Iris segmentation using improved hough transform. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 408–415. Springer, Heidelberg (2012)
Mahlouji, M., Noruzi, A., Kashan, I.: Human iris segmentation for iris recognition in unconstrained environments. IJCSI Int. J. Comput. Sci. 9, 149–155 (2012)
Daugman, J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37, 1167–1175 (2007)
Ryan, W.J., Woodard, D.L., Duchowski, A.T., Birchfield, S.T.: Adapting starburst for elliptical iris segmentation. In: 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2008, pp. 1–7 (2008)
Mehrabian, H., Hashemi-Tari, P.: Pupil boundary detection for iris recognition using graph cuts. In: Proceedings of Image and Vision Computing New Zealand 2007, pp. 77–82 (2007)
Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. 4, 824–836 (2009)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Stat. 28, 337–407 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol.1, pp. I-511–I-518 (2001)
Yao, Y., Li, C.-T.: Hand posture recognition using SURF with adaptive boosting. In: Presented at the British Machine Vision Conference (BMVC), Guildford, United Kingdom (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)
CASIA iris image database: http://biometrics.idealtest.org/
Daugman, J.: How iris recognition works. In: Proceedings of 2002 International Conference on Image Processing, vol. 1, pp. I-33–I-36 (2002)
Acknowledgement
The authors wish to thank the Chinese Academy of Sciences’ Institute of Automation (CASIA) for providing CASIA iris image databases
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Ren, Y., Qu, Z., Liu, X. (2015). A Robust Iris Segmentation Algorithm Using Active Contours Without Edges and Improved Circular Hough Transform. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_39
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DOI: https://doi.org/10.1007/978-3-319-27051-7_39
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