Usefulness of Retina Codes in Biometrics

  • Thomas Fuhrmann
  • Jutta Hämmerle-Uhl
  • Andreas Uhl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


We discuss methods for generating retina codes from retinal images for biometric user authentication. Starting from the optical disc, concentric circles are placed over the binary vessel image for data sampling and different variants of retina code are generated after transformation to polar coordinates. The methods inter personal variability and robustness is evaluated on the publicly available DRIVE database. Results indicate a low inter personal variability questioning the usefulness of retina codes in sensible authentication systems.


Retinal Vessel Morlet Wavelet Biometric System Iris Recognition Vessel Segmentation 
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.
    Bresenham, J.: A linear algorithm for incremental display of circular arcs. Communications of the ACM 20(2), 100–106 (1977)CrossRefzbMATHGoogle Scholar
  2. 2.
    Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  3. 3.
    Soares, J.V.B., Leandro, J.J.G., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging 25(9), 1214–1222 (2006)CrossRefGoogle Scholar
  4. 4.
    Nanavati, S., Thieme, M., Nanavati, R.: Biometrics – Identity verification in a networked world. Wiley Computer Publishing, Chichester (2002)Google Scholar
  5. 5.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circiuts and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  6. 6.
    Ives, R., Guidry, A., Etter, D.: Iris recognition using histogram analysis. In: Conference Record of the 38th Asilomar Conference on Signals, Systems, and Computers, vol. 1, pp. 562–566. IEEE Signal Processing Society, Los Alamitos (2004)Google Scholar
  7. 7.
    Vermeer, K., Vos, F., Lemij, H., Vossepoel, A.: A model based method for retinal blood vessel detection. Computers in Biology and Medicine 34, 209–219 (2004)CrossRefGoogle Scholar
  8. 8.
    Lin, T., Zheng, Y.: Node-matching-based pattern recognition method for retinal blood vessel images. Optical Engineering 42(11), 3302–3306 (2003)CrossRefGoogle Scholar
  9. 9.
    Xu, Z., Guo, X., Hu, X., Chen, X., Wang, Z.: The identification and recognition based on point for blood vessel of ocular fundus. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 770–776. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Barrett, S.F., Naess, E., Molvik, T.: Employing the hough transform to locate the optic disk. Biomedical Sciences Instrumentation 37, 81–86 (2001)Google Scholar
  11. 11.
    Sinthanayothin, C., Boyce, J., Cook, H., Williamson, T.: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology 83, 902–910 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Fuhrmann
    • 1
  • Jutta Hämmerle-Uhl
    • 1
  • Andreas Uhl
    • 1
  1. 1.Department of Computer SciencesSalzburg UniversityAustria

Personalised recommendations