A Self-updating Multiexpert System for Face Identification

  • Andrea F. Abate
  • Maria De Marsico
  • Michele Nappi
  • Daniel Riccio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Multibiometric systems can solve a number of problems of single-biometry approaches. A source of flaws for present systems, both single-biometric and multibiometric, can be found in the lack of dynamic update of parameters, which does not allow them to adapt to changes in the working settings. They are generally calibrated once and for all, so that they are tuned and optimized with respect to standard conditions. In this work we investigate an architecture where single-biometry subsystems work in parallel, yet exchanging information at fixed points, according to the N-Cross Testing Protocol. In particular, the integrated subsystems work on the same biometric feature, the face in this case, yet exploiting different classifiers. Subsystems collaborate at a twofold level, both for returning a common answer and for tuning to changing operating conditions. Results demonstrate that component collaboration increases system accuracy and allows identifying unstable subsystems.


Recognition Rate Face Image Equal Error Rate Neighborhood Preserve Embedding Supervisor Module 
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.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18, 831–836 (1996)CrossRefGoogle Scholar
  3. 3.
    Yan, S., He, X., Cai, D., Zhang, H.-J.: Neighborhood preserving embedding. In: IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1208–1213 (2005)Google Scholar
  4. 4.
    Han, J., Cai, D., He, X., Zhang, H.-J.: Orthogonal laplacianfaces for face recognition. IEEE Transactions on Image Processing 15, 3608–3614 (2006)CrossRefGoogle Scholar
  5. 5.
    He, F., Kouzani, A.Z., Sammut, K.: Fractal face representation and recognition. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1609–1613 (1997)Google Scholar
  6. 6.
    Riccio, D., Tortora, G., Abate, A.F., Nappi, M.: Rbs: A robust bimodal system for face recognition. International Journal of Software Engineering and Knowledge Engineering 17, 497–514 (2007)CrossRefGoogle Scholar
  7. 7.
    Ross, A., Jain, A.K., Qian, J.-Z.: Information fusion in biometrics. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 354–359. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Riccio, D., De Marsico, M., Abate, A.F., Nappi, M.: Data normalization and fusion in multibiometric systems. In: International Conference on Distributed Multimedia Systems, DMS 2007, pp. 87–92 (2007)Google Scholar
  9. 9.
    Riccio, D., De Marsico, M., Abate, A.F., Nappi, M.: Face, ear and fingerprint: Designing multibiometric architectures. In: Proceedings of the 14th International Conference on Image Analysis and Processing - ICIAP 2007, pp. 437–442 (2007)Google Scholar
  10. 10.
    Poh, N., Bengio, S.: Improving fusion with margin-derived confidence in biometric authentication tasks. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 474–483. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Martinez, A.M., Benavente, R.: The ar face database - cvc technical report n.24. Technical Report (1998)Google Scholar
  12. 12.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518 (2001)Google Scholar
  13. 13.
    Opencv. Website, 2008-06-06Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Andrea F. Abate
    • 1
  • Maria De Marsico
    • 2
  • Michele Nappi
    • 1
  • Daniel Riccio
    • 1
  1. 1.DMI - Dipartimento di Matematica e InformaticaUniversità di SalernoFisciano (SA)Italy
  2. 2.DI - Dipartimento di InformaticaSapienza Università di RomaItaly

Personalised recommendations