Abstract
Iris is a new biometric emerging in recent years. Iris identification is gradually applied to a number of important areas because of its simplicity, fast identification and low error recognition rate. Typically, an iris recognition system includes four parts: iris localization, feature extraction, coding and recognition. Among it, iris localization is a critical step. In the paper, a fast iris localization algorithm based on improved Hough transform was proposed. First, the algorithm builds gray histogram of iris image to analyze the gray threshold of the iris boundary. Then takes the pupil image binarization, using corrosion and expansion or region growing to remove noise. As a result, it obtains the radius of the inner edge. Then, we conduct iris location based on Hough transform according to the geometrical feature and gray feature of the human eye image. By narrowing searching scope, localization speed and iris localization accuracy are improved. Besides, it has better robustness for real-time system. Experimental results show that the proposed method is effective and encouraging.
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References
Lu, C.S., Liao, H.M.: Multipurpose watermarking for image authentication and protection. J. IEEE Transaction on Image Processing 10(10), 1579–1592 (2001)
Daugman, J.G.: How iris recognition works. IEEE Transaction on circuits and systems for video technology 14(1), 21–30 (2004)
Daugman, J.G.: High confidence visual identification of person by a test of statistical independence. IEEE Trans. Pattern Anal. Machine Intell. 15(11), 1148–1161 (1993)
Wildes, R.P.: Iris recognition: An emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)
Liu, X., Bowyer, K.W., Flynn, P.J.: Experiments with an Improved Iris Segmentation Algorithm. In: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies, Autoid, October 17 - 18, pp. 118–123. IEEE Computer Society, Washington (2005)
Toennies, K.D., Behrens, F., Aurnhammer, M.: Feasibility of Hough-Transform-based Iris Localisation for Real-Time-Application. In: Internat. Conf. Pattern Recognition, pp. 1053–1056 (2002)
Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A fast and robust iris localization method based on texture segmentation, For Biometric Authentication and Testing, National Laboratory of Pattern Recognition. Chinese Academy of Sciences, 401-408 (2004)
Camus, T., Wildes, R.P.: Reliable and Fast Eye Finding in Close-up Images. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 389–394 (2002)
Wang, Y., Zhu, Y., Tan, T.: Identification based on iris recognition. Acta Automatica Sinica 28(1), 1–10 (2002)
Cui, J., Ma, L., Wang, Y., Tan, T., Sun, Z.: A Fast and Robust Iris Localization Method based on Texture Segmentation. In: SPIE, vol. 5404, pp. 401–408 (2004)
Proenca, H., Alexandre, L.A.: Iris segmentation methodology for non-cooperative recognition. IEEE Proc. Vis. Image Signal Process. 153(2) (2006)
Wildes, R.P., Asmuth, J.C., Green, G.L., et al.: A system for automated iris recognition. In: Proceedings of the IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, pp. 121–128 (1994)
Sun, Y.: A Fast Iris Localization Method Based on Mathematical Morphology. Computer Applications 28(4) (2007)
C.A. of Sciences-Institute of Automation, Database of 756 grey scale eye images (2003), http://www.sinobiometrics.com
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Wang, L., Yang, G., Yin, Y. (2010). Fast Iris Localization Based on Improved Hough Transform. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_62
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DOI: https://doi.org/10.1007/978-3-642-16248-0_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
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