Advertisement

Segmentation for Iris Localisation: A Novel Approach Suitable for Fake Iris Detection

  • Bodade M. Rajesh
  • Talbar N. Sanjay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In iris recognition system, accurate iris segmentation and localisation from eye image is the foremost important step. In this paper a robust and efficient method of iris segmentation is proposed. In the proposed method, the outer boundary of iris is calculated by tracing objects of various shape and structure. Based on the pupil size variation, the inner boundary of iris is detected. The variation in pupil size is also used for aliveness detection of iris. Thus, this approach is a very promising technique in making iris recognition systems more robust against fake-iris-based spoofing attempts. The algorithm is tested on UPOL database of 384 images both eyes of 64 subjects. The success rate of accurate iris localisation from eye image is 99.48% with minimal loss of iris texture features in spatial domain as compared to all existing techniques. The processing time required is also comparable with existing techniques.

Keywords

Iris Segmentation Fake Iris Detection Pupil Dynamics Dynamic Iris Localisation 

References

  1. 1.
    Kong, W., Zhang, D.: Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, May 2001, pp. 263–266 (2001)Google Scholar
  2. 2.
    Wildes, R.: Iris Recognition: An Emerging Biometric Technology. Proc. IEEE 85, 1348–1363 (1997)CrossRefGoogle Scholar
  3. 3.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14, 21–30 (2004)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.: Anti-spoofing Liveness Detection, http://www.cl.cam.ac.uk/users/igdl000/countermeasures.pdf
  5. 5.
    Boles, W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46, 1185–1188 (1998)CrossRefGoogle Scholar
  6. 6.
    Ma, L., Wang, Y., Tan, T.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. on PAMI 25(12), 414–417 (2003)Google Scholar
  7. 7.
    Masek, L., Kovesi, P.: MATLAB source code for a Biometric Identification System Based on Iris Paterns. The school of Computer Science and Software Engineering, The University of Western Austrilia (2003)Google Scholar
  8. 8.
    Narote, S.P., Narote, A.S., Waghmare, L.M.: An automated Segmentation Method for Iris Recognition. In: Proceedings of TENCON 2006. 2006 IEEE Region 10th Conf., November 14-17 (2006)Google Scholar
  9. 9.
    Chinese Academy of Sciences Institute of Automation, Database of 756 Greyscale Eye Images, http://www.sinobiometrics.com
  10. 10.
    Proença, H., Alexandre, L.A.: UBIRIS: iris image database (2004), http://iris.di.ubi.pt
  11. 11.
    High contrast Iris image database downloaded from, http://phoenix.inf.upol.cz/iris/download/

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bodade M. Rajesh
    • 1
  • Talbar N. Sanjay
    • 2
  1. 1.Military College of Telecommunication EngineeringIndia
  2. 2.S.G.G.S. Institute of Engineering and TechnologyVishnupuriIndia

Personalised recommendations