Advertisement

A Testing Methodology for Face Recognition Algorithms

  • Aristodemos Pnevmatikakis
  • Lazaros Polymenakos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3869)

Abstract

Many face recognition methods have been reported in the literature. Also many face databases and face recognition methodologies are available to test them. Unfortunately most authors test their methods using restricted databases, or random subsets of them. This does not facilitate the comparison of the methods. In this paper we propose an evaluation methodology that utilizes three publicly available databases and an evaluation protocol that offers numerous splits of the images between training and testing images. We also evaluate many different face recognition methods using our methodology, offering a comparison between them.

Keywords

Hide Markov Model Face Recognition Training Image Machine Intelligence None None 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Waibe1, A., Steusloff, H., Stiefelhagen, R., et al.: CHIL: Computers in the Human Interaction Loop. In: 5th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Lisbon, Portugal, April 21-23 (2004)Google Scholar
  2. 2.
    Wiskott, L., Fellous, J.-M., Krueger, N., Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. In: Jain, L.C., et al. (eds.) Intelligent Biometric Techniques in Fingerprint and Face Recognition, ch. 11, pp. 355–396. CRC Press, Boca Raton (1999)Google Scholar
  3. 3.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3, 71–86 (March 1991)CrossRefGoogle Scholar
  4. 4.
    Pnevmatikakis, A., Polymenakos, L.: Comparison of Eigenface-Based Feature Vectors under Different Impairments. Int. Conf. Pattern Recognition 2004, 296–300 (August 2004)Google Scholar
  5. 5.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (July 1997)CrossRefGoogle Scholar
  6. 6.
    Bartlett, M., Movellan, J., Sejnowski, T.: Face Recognition by Independent Component Analysis. IEEE Trans. Neural Networks 13(6), 1450–1464 (November 2002)CrossRefGoogle Scholar
  7. 7.
    Xie, C., Vijaya Kumar, B.V.K., Palanivel, S., Yegnanarayana, B.: A Still-to-Video Face Verification System Using Advanced Correlation Filters. In: International Conference on Biometric Authentication, pp. 102–108 (2004)Google Scholar
  8. 8.
    Samaria, F., Harter, A.: Parametrisation of a Stochastic Model for Human Face Identification. In: Proc. 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142 (December 1994)Google Scholar
  9. 9.
    Yang, J., Frangi, A., Yang, J.-Y., Zhang, D., Jin, Z.: KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Transactions On Pattern Analysis And Machine Intelligence 27(2) (February 2005)Google Scholar
  10. 10.
  11. 11.
    Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  13. 13.
    Liu, X., Chen, T., Vijaya Kumar, B.V.K.: On Modeling Variations For Face Authentication. In: International Conference on Automatic Face and Gesture Recognition, pp. 369–374 (May 2002)Google Scholar
  14. 14.
    Philips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  15. 15.
    Martínez, A., Kak, A.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)CrossRefGoogle Scholar
  16. 16.
    Yambor, W., Draper, B., Beveridge, J.: Analyzing PCA-based face recognition algorithms: Eigenvector selection and distance measures. In: Workshop on Empirical Evaluation in Computer Vision, Dublin, Ireland (July 2000)Google Scholar
  17. 17.
    Beveridge, J., She, K., Draper, B., Givens, G.: A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition. In: IEEE Conference on Pattern Recognition and Machine Intelligence, pp. 535–542 (December 2001)Google Scholar
  18. 18.
    Beveridge, J., She, K., Draper, B., Givens, G.: Parametric and nonparametric methods for the statistical evaluation of human id algorithms. In: Workshop on the Empirical Evaluation of Computer Vision Systems (December 2001)Google Scholar
  19. 19.
    Beveridge, J., She, K.: Fall 2001 Update to CSU PCA Versus PCA+LDA Comparison. Tech. Rep., Colorado State University, Fort Collins, CO (December 2001), http://www.cs.colostate.edu/evalfacerec/papers.html
  20. 20.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: Wechsler, Philips, Bruce, Fogelman-Soulie, Huang (eds.) Face Recognition: From Theory to Applications, pp. 73–85 (1998)Google Scholar
  21. 21.
    Zhao, W., Chellappa, R., Phillips, P.: Subspace linear discriminant analysis for face recognition, UMD (1999)Google Scholar
  22. 22.
    Venkatesh, B.S., Palanivel, S., Yegnanarayana, B.: Face Detection and Recognition in an Image Sequence using Eigenedginess. In: 3rd Indian Conference on Vision Graphics and Image Processing, Ahmedabad, December 16-18 (2002)Google Scholar
  23. 23.
    McGill, R., Tukey, J.W., Larsen, W.A.: Variations of Boxplots. The American Statistician, 12–16 (1978)Google Scholar
  24. 24.
    Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)CrossRefzbMATHGoogle Scholar
  25. 25.
    Yang, J., Yang, J.Y.: Why can LDA be performed in the PCA transformed space. Pattern Recognition 36, 563–566 (2003)CrossRefGoogle Scholar
  26. 26.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J.: Face Recognition Using Laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence, 328–340 (March 2005)Google Scholar
  27. 27.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aristodemos Pnevmatikakis
    • 1
  • Lazaros Polymenakos
    • 1
  1. 1.Athens Information Technology, Autonomic and Grid ComputingPeaniaGreece

Personalised recommendations