Abstract
Iris segmentation is one of the most important steps in iris recognition system, many existing localization methods model the iris outer boundary by a circle. However the iris outer boundary are not a circle in case of partially opened eye image. In this paper, we propose a method based on Union-Find-Set to extract the accurate iris boundary. The proposed method have been tested on the a visible light iris database captured by our own laboratory. The experimental results show that the proposed method outperforms the state-of-the-art method not only on localization accuracy rate but also on localization speed.
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References
Jain, A., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)
Daugman, J.: High confidence visual recognition of person by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 1115, 1148–1161 (1993)
Muhammad, T.I., Tarid, M., Shahid, A., et al.: Iris localization using local histogram and other image statistics. Opt. Lasers Eng. 50, 645–654 (2012)
Daugman, J.: The importance of being random: statistical principles of iris recognition. IEEE Trans. Pattern Recogn. 36(2), 279–291 (2003)
Wildes, R.: Iris recognition: an emerging biometric technology. Proc. IEEE 85, 1348–1363 (1997)
Ma, L., Tan, T., Wand, Y., et al.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)
Ma, L., Tan, T., Wang, Y., et al.: Personal recognition based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003)
RodrÃguez, J.L.G., Rubio, Y.D.: A new method for iris pupil contour delimitation and its application in iris texture parameter estimation. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 631–641. Springer, Heidelberg (2005)
Ross, A., Shah, S.: Segmenting non-ideal iris using geodesic active contours. In: Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, Baltimore, USA, pp. 1–6 (2006)
Koh, J., Govindaraju, V., Chaudhary, V.: A robust iris localization method using an active contour model and hough transform. In: 20th International Conference on Pattern Recognition, pp. 2852–2856 (2010)
Chen, R., Lin, X.R., Ding, T.H.: Iris segmentation for non-cooperative recognition system. IET Image Process. 5(5), 448–456 (2011)
Ror, K., Bhattacharya, P., Suen, C.Y.: Iris segmentation using variational level set method. Opt. Laser Eng. 49(4), 578–588 (2011)
Roy, K., Bhattacharya, P., Suen, C.Y.: Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Eng. Appl. Artif. Intell. 24(3), 458–475 (2011)
Connor, B.O., Roy, K.: Iris recognition using level set and local binary pattern. Int. J. Comput. Theor. Eng. 6(5), 416–420 (2014)
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The work was supported by the National Natural Science Foundation of China under Grant 61271365.
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Zhu, L., Yuan, W. (2016). An Accurate Iris Segmentation Method Based on Union-Find-Set. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_39
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DOI: https://doi.org/10.1007/978-3-319-46654-5_39
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