Performance of Correlation Filters in Facial Recognition

  • Everardo Santiago-Ramirez
  • J. A. Gonzalez-Fraga
  • J. I. Ascencio-Lopez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

In this paper, we compare the performance of three composite correlation filters in facial recognition problem. We used the ORL (Olivetti Research Laboratory) facial image database to evaluate K-Law, MACE and ASEF filters performance. Simulations results demonstrate that K-Law nonlinear composite filters evidence the best performance in terms of recognition rate (RR) and, false acceptation rate (FAR). As a result, we observe that correlation filters are able to work well even when the facial image contains distortions such as rotation, partial occlusion and different illumination conditions.

Keywords

Facial Recognition Correlation Filters PSR performance 

References

  1. 1.
    National Science and Technology Council, http://biometrics.gov
  2. 2.
    Vijaya Kumar, B., Mahalanobis, H., Juday, R.: Correlation Pattern Recognition. Cambridge University Press, New York (2005)CrossRefMATHGoogle Scholar
  3. 3.
    Gonzalez-Fraga, J.A., Kober, V., Alvarez Borrego, J.: Adaptive Synthetic Discriminant Function Filters for Pattern Recognition. Optical Engineering 45, 057005 (2006)CrossRefGoogle Scholar
  4. 4.
    VanderLugt, A.B.: Signal detection by complex spatial filtering. IEEE Transactions Information Theory 10, 139–145 (1964)CrossRefGoogle Scholar
  5. 5.
    Casasent, D., Chang, W.: Correlation synthetic discriminant functions. Applied Optics 25, 2343–2350 (1986)CrossRefGoogle Scholar
  6. 6.
    Javidi, B., Wang, W., Zhang, G.: Composite Fourier-plane nonlinear filter for distortion-invariant pattern recognition. Optical Engineering 36, 2690 (1997)CrossRefGoogle Scholar
  7. 7.
    Bolme, D.S., Draper, B.A., Ross Beveridge, J.: Average of Synthetic Exact Filters. Computer Science Department Colorado State University, Fort Collins (2010)Google Scholar
  8. 8.
    Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, Sarasota (1994)Google Scholar
  9. 9.
    Savvides, M., Vijaya Kumar, B.V.: Illumination normalization using logarithm transforms for face authentication. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 549–556. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Sim, T., Kanade, T.: Combining Models and Exemplars for Face Recognition: An Illuminating Example. In: Proceedings of the CVPR (2001)Google Scholar
  11. 11.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition Using Class Specific Linear Projection. In: PAMI-19 (1997)Google Scholar
  12. 12.
    Savvides, M., Vijaya Kumar, B.V., Khosla, P.: Face Verification using Correlation Filters. In: Proc. of the third IEEE Automatic Identification Advanced Technologies, Tarrytown, NY, pp. 56–62 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Everardo Santiago-Ramirez
    • 1
  • J. A. Gonzalez-Fraga
    • 1
  • J. I. Ascencio-Lopez
    • 1
  1. 1.Facultad de CienciasUniversidad Autónoma de Baja CaliforniaBajaMexico

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