Towards a Generalized Eigenspace-Based Face Recognition Framework

  • Javier Ruiz del Solar
  • Pablo Navarrete
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

Eigenspace-based approaches (differential and standard) have shown to be efficient in order to deal with the problem of face recognition. Although differential approaches have a better performance, their computational complexity represents a serious drawback. To overcome that, a post- differential approach, which uses differences between reduced face vectors, is here proposed. The mentioned approaches are compared using the Yale and FERET databases. Finally, a generalized framework is also proposed.

References

  1. 1.
    Burges C. J. C, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, 2(2), pp. 121–167, 1998.CrossRefGoogle Scholar
  2. 2.
    Fisher R. A., “The Use of Multiple Measures in Taxonomic Problems”, Ann. Eugenics, vol. 7, pp. 179–188, 1936.Google Scholar
  3. 3.
    Liu C, and Wechsler H., “Evolutionary Pursuit and Its Application to Face Recognition”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 570–582, June 2000.CrossRefGoogle Scholar
  4. 4.
    Pentland A., and Moghaddam B., “Probabilistic Visual Learning for Object Representation”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696–710, July 1997.CrossRefGoogle Scholar
  5. 5.
    Duda R. O., Hart P. E., and Stork D. G., “Pattern Classification”, Second Edition, 2001.Google Scholar
  6. 6.
    Navarrete P., and Ruiz-del-Solar J., “Comparative study between different Eigenspace-based approaches for Face Recognition”, Lecture Notes in Artificial Intelligence 2275, AFSS 2002, Springer, 178–184.Google Scholar
  7. 7.
    Cortes C, and Vapnik V., “Support Vector Networks”, Machine Learning, 20, pp. 273–297, 1995.MATHGoogle Scholar
  8. 8.
    Phillips P. J., Wechsler H., Huang J., and Rauss P., “The FERET database and evaluation procedure for face recognition algorithms”, Image and Vision Computing J., Vol. 16, no. 5, 295–306, 1998.CrossRefGoogle Scholar
  9. 9.
    Sirovich L., and Kirby M., “A low-dimensional procedure for the characterization of human faces”, J. Opt. Soc. Amer. A, vol. 4, no. 3, pp. 519–524, 1987.CrossRefGoogle Scholar
  10. 10.
    Turk M., and Pentland A., “Eigenfaces for Recognition”, J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.CrossRefGoogle Scholar
  11. 11.
    Yale University Face Image Database, publicly available for non-commercial use, http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Javier Ruiz del Solar
    • 1
  • Pablo Navarrete
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile

Personalised recommendations