Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Iris Segmentation Using Active Contours

  • Sung W. Park
  • Marios Savvides
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_254



Iris segmentation using active contours is finding an iris region in an image using snakes which are curves such as inner and outer boundaries at pupil and sclera. Iris segmentation is a key task for iris recognition. However, it is challenging to detect and exclude the true inner and outer boundaries of an iris. First of all, the occlusion caused by lower and upper eyelids and eyelashes makes it difficult to detect the iris region. Even though eyelids and eyelashes make no occlusion, the inner and outer boundaries are not exact circles, so circle detection such as the Hough transform is not enough to be applied. To obtain the iris boundaries, snakes have been successfully applied in literature. Snakes are energy-minimizing parametric closed curves guided by external forces.


Among various biometric techniques, iris recognition can provide stable and accurate recognition rates, since irises have highly unique pattern...

This is a preview of subscription content, log in to check access.


  1. 1.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, New York (2005)MATHGoogle Scholar
  2. 2.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg, Germany (1988)Google Scholar
  3. 3.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models 1, 321–331 (1988)Google Scholar
  4. 4.
    Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: A survey. University of Nortre Dame CSE Tech. rep., (2007)Google Scholar
  5. 5.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11) (1993)Google Scholar
  6. 6.
    Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  7. 7.
    Thornton, J., Savvides, M., Vijaya Kumar, B.: A bayesian approach to deformed pattern matching of iris images. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 596–606 (2007)CrossRefGoogle Scholar
  8. 8.
    Park, S.: National Institute of Standards and Technology. Iris Challenge Evaluation. http://iris.nist.gov/ice/

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Sung W. Park
    • 1
  • Marios Savvides
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA