Advertisement

Multimodal Biometrics: Topics in Score Fusion

  • Luis Puente
  • M. Jesús Poza
  • Juan Miguel Gómez
  • Diego Carrero
Conference paper
  • 688 Downloads
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 63)

Abstract

This paper describes how the last cutting-edge advances in Computational Intelligence are being applied to the field of biometric security. We analyze multimodal identification systems and particularly the score fusion technique and some issues related with it. Fundamentally, the paper deals with the scores normalization problem in depth, which is one of the most critical issues with a dramatic impact on the final performance of multibiometric systems. Authors show in this paper the results obtained using a number of fusion algorithms (Neural Networks, SVM, Weighted Sum, etc.) on the scores generated with three independent monomodal biometric systems (the modalities are Iris, Signature and Voice). The paper shows the behavior of the most popular score normalization techniques (z-norm, tanh, MAD, etc), and proposes a new score normalization procedure with an optimized performance harnessing tested fusion techniques and outperforming previous results through a proof-of-concept implementation.

Keywords

Score Normalization Multi-Modal biometrics Score Fusion 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Furui, S.: Recent advances in speaker recognition. In: Audio- and Video-based Biometric Person Authentication, pp. 237–252. Springer, Heidelberg (1997)Google Scholar
  2. 2.
    Hong, L., Jain, A.K., Pankanti, S.: Can Multibiometrics Improve Performance? In: Proceedings AutoID 1999, Summit, NJ, October 1999, pp. 59–64 (1999)Google Scholar
  3. 3.
    Sigüenza, A., Tapiador, M.: Tecnologías Biométricas Aplicadas a la Seguridad. Editorial Ra-Ma (2005)Google Scholar
  4. 4.
    Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. Circuits Syst. Video Technology, Special Issue Image- and Video-Based Biomet. 14(1), 4–20 (2004)Google Scholar
  5. 5.
    Ross, A., Jain, A.K.: Information Fusion in Biometrics. Pattern Recognition 24(13), 2115–2125 (2003)CrossRefGoogle Scholar
  6. 6.
    Huang, Y., Suen, C.: A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. IEEE Trans. on PAMI 17, 90–94 (1995)Google Scholar
  7. 7.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Transacations on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  8. 8.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  9. 9.
    Vapnic, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)Google Scholar
  10. 10.
    Miguel-Hurtado, O., Mengibar-Pozo, L., Lorenz, M.G., Liu-Jimenez, J.: On-Line Signature Verification by Dynamic Time Warping and Gaussian Mixture Models. In: IEEE Proc. Int. Carnahan Conference on Security Technology, October 2007, pp. 23–29 (2007)Google Scholar
  11. 11.
    Sanchez-Avila, C., Sanchez-Reillo, R.: Two Different Approaches for Iris Recognition using Gabor Filters and Multiscale Zero-crossing Representation. Pattern Recognition 38, 231–240 (2005)CrossRefGoogle Scholar
  12. 12.
    Ortega-García, J., et al.: MCYT baseline corpus: a bimodal biometric database. IEE Proceedings Vision, Image and Signal Processing 150(6), 395–401 (2003)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Univerzita Palackého Iris Database, http://www.inf.upol.cz/iris/
  15. 15.
    NIST ICE2005 Iris Database, http://iris.nist.gov/ICE/
  16. 16.
    Fernandez-Saavedra, B., Liu-Jimenez, J., Sanchez-Avila, C.: Quality Measurements for Iris Images for Biometrics. In: IEEE EUROCON 2007 International Conference on “Computer as a tool”, Warsaw, Poland (September 2007)Google Scholar
  17. 17.
    Przybocki, M.A., Martin, A.F., Le, A.N.: NIST speaker recognition evaluations utilizing the mixer corpora-2004,2005,2006. IEEE trans. on Audio, Speech and Language Processing 15(7) (September 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis Puente
    • 1
  • M. Jesús Poza
    • 1
  • Juan Miguel Gómez
    • 1
  • Diego Carrero
    • 1
  1. 1.Universidad Carlos III de MadridLeganés, MadridSpain

Personalised recommendations