Advertisement

Robust Iris Localisation in Challenging Scenarios

  • João C. MonteiroEmail author
  • Ana F. Sequeira
  • Hélder P. Oliveira
  • Jaime S. Cardoso
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 458)

Abstract

The use of images acquired in unconstrained scenarios is giving rise to new challenges in the field of iris recognition. Many works in literature reported excellent results in both iris segmentation and recognition but mostly with images acquired in controlled conditions. The intention to broaden the field of application of iris recognition, such as airport security or personal identification in mobile devices, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focuses on mutual context information from iris centre and iris limbic and pupillary contours to perform robust and accurate iris segmentation in noisy images. The developed algorithm was tested on the MobBIO database with a promising \(96\,\%\) segmentation accuracy for the limbic contour.

Keywords

Biometrics Iris segmentation Unconstrained environment Gradient flow Shortest closed path 

Notes

Acknowledgements

The authors would like to thank Fundação para a Ciência e Tecnologia (FCT) - Portugal the financial support for the PhD grants with references SFRH/ BD/74263/2010 and SFRH/BD/87392/2012.

References

  1. 1.
    Abhyankar, A., Schuckers, S.: Iris quality assessment and bi-orthogonal wavelet based encoding for recognition. Pattern Recogn. 42(9), 1878–1894 (2009)CrossRefzbMATHGoogle Scholar
  2. 2.
    Barzegar, N., Moin, M.: A new approach for iris localisation in iris recognition systems. In: Proceedings of the International Conference on Computer Systems and Applications, pp. 516–523 (2008)Google Scholar
  3. 3.
    Chen, R., Lin, X., Ding, T.: Iris segmentation for non-cooperative recognition systems. Image Process. 5(5), 448–456 (2011)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N., Andrian, J.: A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vis. Comput. 28(2), 261–269 (2010)CrossRefGoogle Scholar
  5. 5.
    Daugman, J.: How iris recognition works. In: Proceedings of the International Conference on Image Processing. vol. 1, pp. I-33–I-36 (2002)Google Scholar
  6. 6.
    Daugman, J.: Probing the uniqueness and randomness of iriscodes: results from 200 billion iris pair comparisons. Proc. IEEE 94(11), 1927–1935 (2006)CrossRefGoogle Scholar
  7. 7.
    Daugman, J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1167–1175 (2007)CrossRefGoogle Scholar
  8. 8.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  9. 9.
    He, Z., Tan, T., Sun, Z., Qiu, X.: Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1670–1684 (2009)CrossRefGoogle Scholar
  10. 10.
    Houhou, N., Lemkaddem, A., Duay, V., Alla, A., Thiran, J.P.: Shape prior based on statistical map for active contour segmentation. In: 15th IEEE International Conference on Image Processing, pp. 2284–2287 (2008)Google Scholar
  11. 11.
    Jain, A., Hong, L., Pankanti, S.: Biometric identification. Commun. ACM 43(2), 90–98 (2000)CrossRefGoogle Scholar
  12. 12.
    Kobatake, H., Hashimoto, S.: Convergence index filter for vector fields. IEEE Trans. Image Process. 8(8), 1029–1038 (1999)CrossRefGoogle Scholar
  13. 13.
    Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image Vis. Comput. 28(2), 246–253 (2010)CrossRefGoogle Scholar
  14. 14.
    Lu, C., Lu, Z.: Local feature extraction for iris recognition with automatic scale selection. Image Vis. Comput. 26(7), 935–940 (2008)CrossRefGoogle Scholar
  15. 15.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Local intensity variation analysis for iris recognition. Pattern Recogn. 37(6), 1287–1298 (2004)CrossRefGoogle Scholar
  16. 16.
    Masek, L.: Recognition of human iris patterns for biometric identification. Towards non-cooperative biometric iris recognition. Ph.D. thesis (2003)Google Scholar
  17. 17.
    Monteiro, J.C., Oliveira, H.P., Rebelo, A., Sequeira, A.F.: MobBIO 2013: 1st Biometric Recognition with Portable Devices Competition (2013). http://paginas.fe.up.pt/~mobBIO2013/
  18. 18.
    Monteiro, J.C., Oliveira, H.P., Sequeira, A.F., Cardoso, J.S.: Robust iris segmentation under unconstrained settings. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), pp. 180–190 (2013)Google Scholar
  19. 19.
    Oliveira, H., Cardoso, J., Magalhaes, A., Cardoso, M.: Simultaneous detection of prominent points on breast cancer conservative treatment images. In: Proceedings of the 19th IEEE International Conference on Image Processing. pp. 2841–2844 (2012)Google Scholar
  20. 20.
    Pawar, M., Lokande, S., Bapat, V.: Iris segmentation using geodesic active contour for improved texture extraction in recognition. Int. J. Comput. Appl. 47(16), 448–456 (2012)Google Scholar
  21. 21.
    Proença, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The ubiris.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010)CrossRefGoogle Scholar
  22. 22.
    Radman, A., Jumari, K., Zainal, N.: Iris segmentation in visible wavelength environment. Proc. Eng. 41, 743–748 (2012)CrossRefGoogle Scholar
  23. 23.
    Ross, A.: Iris recognition: the path forward. Computer 43(2), 30–35 (2010)CrossRefGoogle Scholar
  24. 24.
    Roy, K., Bhattacharya, P., Suen, C., You, J.: Recognition of unideal iris images using region-based active contour model and game theory. In: 17th IEEE International Conference on Image Processing. pp. 1705–1708 (2010)Google Scholar
  25. 25.
    Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. 4(4), 824–836 (2009)CrossRefGoogle Scholar
  26. 26.
    Tan, T., He, Z., Sun, Z.: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis. Comput. 28(2), 223–230 (2010)CrossRefGoogle Scholar
  27. 27.
    Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. B Cybern. 38(4), 1021–1035 (2008)CrossRefGoogle Scholar
  28. 28.
    Wildes, R.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  29. 29.
    Zuo, J., Schmid, N.: On a methodology for robust segmentation of nonideal iris images. IEEE Trans. Syst. Man Cybern. B Cybern. 40(3), 703–718 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • João C. Monteiro
    • 1
    Email author
  • Ana F. Sequeira
    • 1
  • Hélder P. Oliveira
    • 1
  • Jaime S. Cardoso
    • 1
  1. 1.INESC TEC and Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations