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An Efficient Iris Segmentation Method for Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

Abstract

In this paper, an efficient iris segmentation method for recognition is described. The method is based on crossed chord theorem and zigzag collarette area. We select the zigzag collarette region as personal identification pattern, which can remove unnecessary areas and get good recognition rate. Zigzag collarette area is one of the most important parts of iris complex pattern. It is insensitive to the pupil dilation and not affected by the eyelid or eyelash since it is closed with the pupil. In our algorithm, we could avoid procedure for eyelid detection and searching the radius and the center position of the outer boundary between the iris and the sclera, which is difficult to locate when there is little contrast between iris and sclera regions. The method was implemented and tested using two iris database sets, i.e CASIA and SJTU-IDB, with different contrast quality. The experimental results show that the performance of the proposed method is encouraging and comparable to the traditional method.

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References

  1. Daugman, J.G.: High Confidence Visual Recognition of persons by a Test of Statistical Independence. IEEE Transaction on Pattern Analysis and Machine Intelligence 15(11), 1148–1160 (1993)

    Article  Google Scholar 

  2. Daugman, J.G.: The importance of being random: Statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing 13(6), 739–750 (2004)

    Article  Google Scholar 

  6. Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Recognition Based on Iris Texture Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)

    Article  Google Scholar 

  7. Huang, J.Z., Wang, Y.H., Tan, T., et al.: A new iris segmentation method for recognition. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, pp. 554–557 (2004)

    Google Scholar 

  8. Kovesi, P.: Image Features From Phase Congruency. Videre: A Journal of Computer Vision Research  1(3), MIT Press (Summer 1999)

    Google Scholar 

  9. CASIA Iris Image Database, http://www.sinobiometrics.com

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© 2005 Springer-Verlag Berlin Heidelberg

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He, X., Shi, P. (2005). An Efficient Iris Segmentation Method for Recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_14

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  • DOI: https://doi.org/10.1007/11552499_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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