Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Applying a novelty filter as a matching criterion to iris recognition for binary and real-valued feature vectors

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The main contributions of this paper are proposing a robust matching measure that employs multiple images of a subject to enroll an iris and that can be used with both types of feature vectors, real-valued and binary feature vectors. The first one is obtained using wavelet transforms and pixel intensity images and the second one using binary wavelet coefficients. The validation of the new matching measure proposed was done considering two utilization modes of the biometric system: verification mode and identification mode. The performance of the new matching measure is comparable to other published results. The vector with lower size was the one that uses binary wavelet coefficients, with only 10 bytes of information. Other authors reported binary feature vector sizes much greater than this one. Iris codification with vectors of lower sizes accounts for the construction of iris recognition embedded systems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kronfeld P.: Gross anatomy and embryology of the eye. In: Davson, H (eds) The Eye, Academic Press, London (1962)

    Google Scholar 

  2. Flom, L., Safir, A.: Iris recognition system, U.S Patent 4,641,394, (1987)

  3. Daugman, J.: Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560, March (1994)

  4. Wildes R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  5. Bolle, R.M., Pankanti, S., Connell, J.H., Ratha, N.: Iris individuality: a partial iris model. In: International Conference on Pattern Recognition, pp. II: 927–930 (2004)

  6. Pacut, A., Czajka, A., Strzelczyk, P.: Iris biometrics for secure remote access. In: Cyberspace Security and Defense: Research Issues NATO Science Series II: Mathematics, Physics and Chemistry, vol. 196, Springer, Berlin (2004)

  7. Huang, H., Hu, G.: Iris recognition based on adjustable scale wavelet transform. In: International Conference on Engineering in Medicine and Biology, pp. 7533–7536 (2005)

  8. Boles W., Boashash B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process 46(4), 1185–1188 (1998)

    Article  Google Scholar 

  9. Sanchez-Avila, C., Sanchez-Reillo, R.: Multiscale analysis for iris biometrics. In: IEEE International Carnahan Conference on Security Technology, pp. 35–38 (2002)

  10. Gan, J., Liang, Y.: Applications of wavelet packets decomposition in iris recognition. In: Springer LNCS 3832: International Conference on Biometrics, pp. 443–449. January (2006)

  11. Jang, J., Park, K.R., Son, J., Lee, Y.: A study on multi-unit iris recognition. In: International Conference on Control, Automation, Robotics and Vision, vol. 2, pp. 1244–1249. December (2004)

  12. Bowyer, K.W., Chang, K.I., Yan, P., Flynn, P.J., Hansley, E., Sarkar, S.: Multi-modal biometrics: an overview. In: Second Workshop on Multi-Modal User Authentication, May (2006)

  13. Chang, K.I., Bowyer, K.W., Flynn, P.J.: An evaluation of multi-modal 2D + 3D face biometrics. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 619–624, April (2005)

  14. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Worek, W.: Preliminary face recognition grand challenge results. In: International Conference on Automatic Face and Gesture Recognition (FG 2006), April (2006)

  15. Ma L., Tan T., Wang Y., Zhang D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process 13(6), 739–750 (2004)

    Article  Google Scholar 

  16. Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. In: Computer Vision and Image Understanding, vol.110, pp. 281–307 (2008)

  17. UBIRIS—Noisy visible wavelength iris image databases. Available at: http://iris.di.ubi.pt/

  18. Costa Filho, C.F.F., Costa, M.G.F.: Iris segmentation exploring color spaces. In: The 3rd International Congress on Image and Signal Processing (CISP’10). 16–18 Sept., Yantai, China. Unpublished paper (2010)

  19. Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: An efficient iris recognition algorithm using phase-based image matching. In: International Conference on Image Processing, pp. II:49–52 (2005)

  20. Sanchez-Reillo, R., Sanchez-Avila, C.: Iris recognition with low template size. In: International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 324–329 (2001)

  21. Elsherief, S.M., Allam, M.E., Fakhr, M.W.: Biometric personal identification based on iris recognition. In: Allam, M.E. (ed.) Computer Engineering and Systems, The 2006 International Conference, pp. 208–213 (2006)

  22. Kohonen T.: Self-Organization and Associative Memory. Springer, New York (1983)

    Google Scholar 

  23. Jain A.K., Ross A., Prabhakar S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol, Special Issue on Image- and Video-Based Biometrics 14(1), 4–20 (2004)

    Article  Google Scholar 

  24. Proenca H., Alexandre L.A.: Toward noncooperative iris recognition: a classification approach using multiple signatures. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 607–612 (2007)

    Article  Google Scholar 

  25. Monro D.M., Rakshit S., Zhang D.: DCT-based iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 586–595 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Federal University of Amazonas, Av. General Rodrigo Otavio Jordao Ramos, 3000, Aleixo, Campus Universitario, Manaus, AM, CEP 69077-000, Brazil

    Cicero Ferreira Fernandes Costa Filho & Marly Guimaraes Fernandes Costa

  2. Science, Technology and Innovation Center for the Manaus Industrial Pole, Rua Salvador, 391—Adrianopolis, Manaus, Amazonas, CEP 69057-040, Brazil

    Claudio Franklin Martins Pinheiro

  3. Biomedical Engineering Program—COPPE/UFRJ, Caixa Postal 68510, Rio de Janeiro, RJ, CEP 21941-972, Brazil

    Wagner Coelho de Albuquerque Pereira

Authors
  1. Cicero Ferreira Fernandes Costa Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claudio Franklin Martins Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marly Guimaraes Fernandes Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wagner Coelho de Albuquerque Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cicero Ferreira Fernandes Costa Filho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Costa Filho, C.F.F., Pinheiro, C.F.M., Costa, M.G.F. et al. Applying a novelty filter as a matching criterion to iris recognition for binary and real-valued feature vectors. SIViP 7, 287–296 (2013). https://doi.org/10.1007/s11760-011-0237-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-011-0237-5

Keywords

Search

Navigation