An Accurate and Fast Iris Location Method Based on the Features of Human Eyes

  • Weiqi Yuan
  • Lu Xu
  • Zhonghua Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)


In this paper, we proposed an accurate and fast iris location method based on the features of human eyes. Firstly, according to the gray features of pupil, find a point inside the pupil using a gray value summing operator. Next, starting from this point, find three points on the iris inner boundary using a boundary detection template designed by ourselves, and then calculate the circle parameters of iris inner boundary according to the principle that three points which are not on the same line can define a circle. Finally, find other three points on the iris outer boundary utilizing the similar method and obtain the circle parameters. A large number of experiments on the CASIA iris image database demonstrated that the location results of proposed method are more accurate than any other classical methods, such as Daugman’s algorithm and Hough transforms, and the location speed is very fast.


Boundary Point Outer Boundary Iris Image Biometric Identification Iris Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, M., Pan, Q., Zhang, H., Zhang, S.: Multibiometrics Identification Techniques. Information and Control 31(6), 524–528 (2002)Google Scholar
  2. 2.
    Jain, A.K., Bolle, R.M., Pankanti, S.: Biometrics: Personal Identification in a Networked Society. Kluwer, Norwell (1999)Google Scholar
  3. 3.
    Daugman, J.G.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(1), 1148–1161 (1993)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.G.: Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns. International Journal of Computer Vision 45(1), 25–38 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Daugman, J.G.: How Iris Recognition Works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  6. 6.
    Wildes, R.P.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  7. 7.
    Wildes, R.P., Asmuth, J., et al.: A Machine-vision System for Iris Recognition. Machine Vision and Applications 9, 1–8 (1996)CrossRefGoogle Scholar
  8. 8.
    Boles, W.W., Boashah, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transactions on Signal Processing 46, 1185–1188 (1998)CrossRefGoogle Scholar
  9. 9.
    Ma, L., Wang, Y., Tan, T.: Iris Recognition Based on Multichannel Gabor Filters. In: Proceedings of the Fifth Asian Conference on Computer Vision, vol. I, pp. 279–283 (2002)Google Scholar
  10. 10.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient Iris Recognition through Improvement of Feature Vector and Classifier. ETRI Journal 23(2), 61–70 (2001)CrossRefGoogle Scholar
  13. 13.
    Sanchez-Avila, C., Sanchez-Reillo, R.: Iris-based Biometric Recognition Using Wavelet Transforms. IEEE Aerospace and Electronic Systems Magazine, 3–6 (2002)Google Scholar
  14. 14.
    Kwanghyuk, B., Seungin, N., Kim, J.: Iris Feature Extraction Using Independent Component Analysis. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 838–844. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Wang, C., Ye, H.: Investigation of Iris Identification Algorithm. Journal of Guizhou University of Technology (Natural Science Edition) 29(3), 48–52 (2000)Google Scholar
  16. 16.
    Chen, L., Ye, H.: A New Iris Identification Algorithm. Journal of Test and Measurement Technology 14(4), 211–216 (2000)Google Scholar
  17. 17.
    Hu, Z., Wang, C., Yu, L.: Iris Location Using Improved Randomized Hough Transform. Chinese Journal of Scientific Instrument 24(5), 477–479 (2003)Google Scholar
  18. 18.
    Yu, X.: The Study of Iris-orientation Algorithm. Journal of Tianjin University of Science and Technology 19(3), 49–51 (2004)Google Scholar
  19. 19.
    Huang, X., Liu, H.: Research on Iris Location Technique Based on Gray Gradient. Journal of Kunming University of Science and Technology 26(6), 32–34 (2001)Google Scholar
  20. 20.
    Wang, C., Hu, Z., Lian, Q.: An Iris Location Algorithm. Journal of Computer-aided Design and Computer Graphics 14(10), 950–952 (2002)Google Scholar
  21. 21.
    Wang, Y., Zhu, Y., Tan, T.: Biometrics Personal Identification Based on Iris Pattern. Acta Automatica Sinica 28(1), 1–10 (2002)Google Scholar
  22. 22.
    Ye, X., Zhuang, Z., Zhang, Y.: A New and Fast Algorithm of Iris Location. Computer Engineering and Applications 30, 54–56 (2003)Google Scholar
  23. 23.
    Institute of Automation, Chinese Academy of Sciences, CASIA Iris Image Database (ver 1.0),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Weiqi Yuan
    • 1
  • Lu Xu
    • 1
  • Zhonghua Lin
    • 1
  1. 1.Computer Vision GroupShenyang University of TechnologyShenyangChina

Personalised recommendations