A Novel Method for Coarse Iris Classification

  • Li Yu
  • Kuanquan Wang
  • David Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


This paper proposes a novel method for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases and the accuracy is 94.61%. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.


Fractal Dimension Search Time Iris Image Image Block Border Effect 
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.


  1. 1.
    Daugman, J.G.: High Confidential Visual Recognition by Test of Statistical Independence. IEEE Trans. PAMI 15(11), 1148–1161 (1993)Google Scholar
  2. 2.
    Wildes, R.P.: Iris Recognition: an Emerging Biometric Technology. Proc. IEEE 85, 1348–1363 (1997)CrossRefGoogle Scholar
  3. 3.
    Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46(4), 1185–1188 (1998)CrossRefGoogle Scholar
  4. 4.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. PAMI 25(12), 1519–1533 (2003)Google Scholar
  5. 5.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Local Intensity Variation Analysis for Iris Recognition. Pattern Recognition 37, 1287–1298 (2004)CrossRefGoogle Scholar
  6. 6.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recognition by Characterizing Key local Variations. IEEE Trans. Image Processing 13(6), 739–749 (2004)CrossRefGoogle Scholar
  7. 7.
    Mandelbrot, B.B., Van Ness, J.W.: Fractional Brownian motions, fractional noises and applications. SIAM Rev. 10(4), 422–437 (1968)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Peitgen, H.O., Jurgens, H., Saupe, D.: Chaos and Fractals New Frontiers of Science, Berlin, Germany, pp. 202–213. Springer, Heidelberg (1992)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Li Yu
    • 1
  • Kuanquan Wang
    • 1
  • David Zhang
    • 2
  1. 1.Department of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of computingThe Hong Kong Polytechnic UniversityKowloon, Hong Kong

Personalised recommendations