Iris Recognition: Localization, Segmentation and Feature Extraction Based on Gabor Transform

  • Mohammadreza Noruzi
  • Mansour Vafadoost
  • M. Shahram Moin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


Iris recognition is one of the best methods in the biometric field. It includes two main processes: “Iris localization and segmentation” and “Feature extraction and coding”. We have introduced a new method based on Gabor transform for localization and segmentation of iris in eye image and also have used it to implement an Iris Recognition system. By applying the Gabor transform to an eye image, some constant templates are extracted related to the borders of pupil and iris. These features are robust and almost easy to use. There is no restriction and no tuning parameter in algorithm. The algorithm is extremely robust to the eyelids and eyelashes occlusions. To evaluate the segmentation method, we have also developed a gradient based method. The results of experimentations show that our proposed algorithm works better than the gradient based algorithm. The results of our recognition system are also noticeable. The low FRR and FAR values justify the results of segmentation method. We have also applied different Gabor Wavelet filters for feature extraction. The observations show that the threshold used to discriminate feature vectors is highly dependant on the orientation, scale and parameters of the corresponding Gabor Wavelet Transform.


Feature Vector Feature Extraction Gabor Filter Iris Image Gabor Wavelet 
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.
    Wilds, R.P.: Iris recognition: An emerging biometric technology. Proceeding of IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  3. 3.
    Chen, D.: Joint Time-Frequency Analysis. Prentice-Hall, Englewood Cliffs (1996)Google Scholar
  4. 4.
    Heitger, F., Rosenthaler, L., von der Heydt, R., Peterhans, E., Kubler, O.: Simulation of neural contour mechanisms: from simple to end-stopped cells. Vision Research 32, 963–981 (1992)CrossRefGoogle Scholar
  5. 5.
    MacLennan, B.: Gabor representation of spatiotemporal visual images. Tech. Report, CS-91-144. Comp. Science Dept., Univ. Tennessee (1994)Google Scholar
  6. 6.
    Lee, T.: Image representation using 2D Gabor wavelets. IEEE Trans- actions on Pattern Analysis and Machine Intelligence 18, 959–971 (1996)CrossRefGoogle Scholar
  7. 7.
    Ma, L., Wang, Y., Tan, T.: Iris Recognition Based on Multichannel Gabor Filtering. In: Proceedings of ACCV, Australia, vol. I, pp. 279–283 (2002)Google Scholar
  8. 8.
    Ma, L., Wang, Y., Tan, T.: Iris Recognition Using Circular Symmetric Filters. In: IEEE International Conference on Pattern Recognition, Canada, vol. II, pp. 414–417 (2002)Google Scholar
  9. 9.
    Tisse, C., Martin, L.: Person Identification Technique Using Human Iris Recognition. In: Proc. of Vision Interface, pp. 294–299 (2002)Google Scholar
  10. 10.
    Sanchez-Reillo, R., Sanchez-Avila, C.: Iris Recognition with Low Template Size. In: Proc. of Audio and Video Based Biometric Person Authentication, pp. 324–329 (2001)Google Scholar
  11. 11.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. II, pp. 316–317. Addison-Wesley, Reading (1992)Google Scholar
  12. 12.
    Ma, L.: Local Intensity Variation Analysis for Iris Recognition. Pattern Recognition 37(6), 1287–1298 (2004)CrossRefGoogle Scholar
  13. 13.
    Ma, L., Tieniu, T.: Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Trans. on Image Processing 13(6), 739–750 (2004)CrossRefGoogle Scholar
  14. 14.
    Ajdari-Rad, A., Safabakhsh, R.: Fast Iris and Pupil Localization and Eyelid Removal Using Gradient Vector Pairs and Certainty Factors. In: Proc. of Conf. Machine Vision and Image Processing, pp. 82–91 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohammadreza Noruzi
    • 1
  • Mansour Vafadoost
    • 1
  • M. Shahram Moin
    • 2
  1. 1.Biomedical Engineering FacultyAmirkabir University of TechnologyTehranIran
  2. 2.Multimedia Dept., IT Research FacultyIran Telecom. Research CenterTehranIran

Personalised recommendations