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Adaptive Iris Segmentation

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Advances in Information Security and Assurance (ISA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 5576))

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Abstract

In this paper an adaptive iris segmentation algorithm is presented. In the proposed algorithm Otsu Threshold value, average gray level of the image, image size, Hough-Circle search are used for adaptive segmentation of irises. Otsu threshold is used for selecting threshold value in order to determine pupil location. Then Hough circle is utilized for pupillary boundary, and finally gradient search is used for the limbic boundary detection. The algorithm achieved 98% segmentation rate in batch processing of the CASIA version 1 (756 Images) and version 3 (CASIA-IrisV3-Interval, 2655 Images) databases.

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References

  1. Jain, A., Bolle, R., Kanti, S.P.: Biometrics: Personal Identification in a Networked Society. Kluwer, Dordrecht (1998)

    Google Scholar 

  2. Adler, F.: Physiology of the Eye: Clinical Application, 4th edn. The C.V. Mosby Company, London (1965)

    Google Scholar 

  3. Daugman, J.: Biometric Personal Identification System Based on Iris Analysis. US Patent no. 5291560 (1994)

    Google Scholar 

  4. Daugman, J.: Statistical richness of visual phase information: Update on recognizing persons by iris patterns. Int. Journal of Computer Vision (2001)

    Google Scholar 

  5. Daugman, J.: Demodulation by complex-valued wavelets for stochastic pattern recognition. Int. Journal of Wavelets, Multiresolution and Information Processing (2003)

    Google Scholar 

  6. Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)

    Article  Google Scholar 

  7. Wildes, R.: Iris recognition: An emerging biometric technology. Proc. of the IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  8. Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. on Signal Processing 46(4), 1185–1188 (1998)

    Article  Google Scholar 

  9. Masek, L.: Recognition of Human Iris Patterns for Biometric Identification. BEng. Thesis. School of Computer Science and Software Engineering, The University of Western Australia (2003)

    Google Scholar 

  10. Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intelligence 25(12), 1519–1533 (2003)

    Article  Google Scholar 

  11. Ma, L., Wang, Y.H., Tan, T.N.: Iris recognition based on multichannel gabor filtering. In: Proc. of the Fifth Asian Conference on Computer Vision, Australia, pp. 279–283 (2002)

    Google Scholar 

  12. Tisse, C., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proc. of Vision Interface, pp. 294–299 (2002)

    Google Scholar 

  13. Kanag, H., Xu, G.: Iris recognition system. Journal of Circuit and Systems 15(1), 11–15 (2000)

    Google Scholar 

  14. Yuan, W., Lin, Z., Xu, L.: A rapid iris location method based on the structure of human eyes. In: Proc. of 27th IEEE Annual Conferemce Engineering in Medicine and Biology, Shanghai, China, September 1-4 (2005)

    Google Scholar 

  15. Daugman, J.: New methods in iris recognition. IEEE Trans. Syst., Man, Cybern. B, Cybern. 37(5), 1168–1176 (2007)

    Article  Google Scholar 

  16. Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. on Systems, Man, and Cybernetics Part B: Cybernetics 38(4), 1021–1035 (2008)

    Article  Google Scholar 

  17. Liu, X., Bowyer, K., Flynn, P.: Experiments with an improved iris segmentation algorithm. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies, vol. 17-18, pp. 118–123 (2005)

    Google Scholar 

  18. Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A fast and robust iris localization method based on texture segmentation. In: Proc. SPIE, vol. 5404, pp. 401–408 (2004)

    Google Scholar 

  19. Abiyev, R., Altunkaya, K.: Neural Network Based Biometric Personel Identification with fast iris segmentation. Int. Journal of Control, Automation and Systems. 7(1) (2009)

    Google Scholar 

  20. Abiyev, R., Altunkaya, K.: Iris recognition for biometric personal identification using neural networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 554–563. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Daugman, J., Downing, C.: Recognizing iris texture by phase demodulation. In: IEEE Colloquium on Image Processing for Biometric Measurement, vol. 2, pp. 1–8 (1994)

    Google Scholar 

  22. Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: An effective approach for iris recognition using phase-based image matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(10), 1741–1756 (2008)

    Article  Google Scholar 

  23. Sanchez-Avila, C., Sanchez-Reillo, R.: Iris-based biometric recognition using dyadic wavelet transform. IEEE Aerospace and Electronic Systems Magazine, 3–6 (2002)

    Google Scholar 

  24. Noh, S., Bae, K., Kim, J.: A novel method to extract features for iris recognition system. In: Proc. 4th Int. Conf. Audio and Video Based Biometric Person Authentication, pp. 838–844 (2003)

    Google Scholar 

  25. Mallat, S.: Zero crossings of a wavelet transform. IEEE Trans. Inf. Theory 37(4), 1019–1033 (1992)

    Article  MathSciNet  Google Scholar 

  26. Park, C., Lee, J., Smith, M., Park, K.: Iris based personal authentication using a normalized directional energy feature. In: Proc. 4th Int. Conf. Audio- and Video-Based Biometric Person Authentication, pp. 224–232 (2003)

    Google Scholar 

  27. Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improvement of feature vector and classifier. ETRI J. 23(2), 61–70 (2001)

    Article  Google Scholar 

  28. Wang, Y., Han, J.Q.: Iris feature extraction using independent component analysis. In: Proc. 4th Int. Conf. Audio and Video Based Biometric Person Authentication, pp. 838–844 (2003)

    Google Scholar 

  29. Wang, Y., Han, J.Q.: Iris recognition using independent component analysis. In: Proc. of the Fourth Int. Conf. on Machine Learning and Cybernetics, Guangzhou (2005)

    Google Scholar 

  30. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9, 62–66 (1979)

    Article  Google Scholar 

  31. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)

    Article  Google Scholar 

  32. Trier, I.D., Taxt, T.: Evaluation of binarization methods for document images. IEEE Trans. on Pattern Analysis and Machine Intelligence (1995)

    Google Scholar 

  33. Zuo, J., Schmid, N.: An Automatic Algorithm for Evaluating the Precision of Iris Segmentation. In: IEEE Second Int. Conf. on Biometrics Theory, Applications and Systems (BTAS 2008), September 29 - October 1 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Near East University, Nicosia, Cyprus

    Rahib Abiyev & Kemal Kilic

Authors
  1. Rahib Abiyev
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  2. Kemal Kilic
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Editor information

Editors and Affiliations

  1. Computer Engineering Department, Kyungnam University, 449, Wolyong-dong, 631-701, Kyungnam, Masan, Korea

    Jong Hyuk Park

  2. Institute of Communications Engineering, National Sun Yat-Sen University, Kaohsiung City, Taiwan

    Hsiao-Hwa Chen

  3. School of Computer Science,, University of Oklahoma, 200 Felgar Street, 73019, P.O. Box, Norman, OK, USA

    Mohammed Atiquzzaman

  4. School of Computer Engineering, Hanshin University, Kyeong-Gi, Osan, Korea

    Changhoon Lee

  5. Division of Multimedia, Hannam University, Daejeon, Korea

    Tai-hoon Kim

  6. Division of Computer Engineering, Mokwon University, Daejeon, Korea

    Sang-Soo Yeo

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© 2009 Springer-Verlag Berlin Heidelberg

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Abiyev, R., Kilic, K. (2009). Adaptive Iris Segmentation. In: Park, J.H., Chen, HH., Atiquzzaman, M., Lee, C., Kim, Th., Yeo, SS. (eds) Advances in Information Security and Assurance. ISA 2009. Lecture Notes in Computer Science, vol 5576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02617-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-02617-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02616-4

  • Online ISBN: 978-3-642-02617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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