Design of a Feature Set for Face Recognition Problem

  • Emre Akbaş
  • Fatoş T. Yarman-Vural
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


An important problem in face recognition is the design of the feature space which represents the human face. Various feature sets have been and are continually being proposed for this purpose. However, there exists no feature set which gives a superior and consistent recognition performance on various face databases. Concatenating the popular features together and forming a high dimensional feature space introduces the curse of dimensionality problem. For this reason, dimensionality reduction techniques such as Principal Component Analysis is utilized on the feature space. In this study, first, some of the popular feature sets used in face recognition literature are evaluated over three popular face databases, namely ORL [1], UMIST [2], and Yale [3]. Then, high dimensional feature space obtained by concatenating all the features is reduced to a lower dimensional space by using the Minimal Redundancy Maximal Relevance [4] feature selection method in order to design a generic and successful feature set. The results indicate that mRMR selects a small number of features which are satisfactory and consistent in terms of recognition performance, provided that the face database is statistically stable with sufficient amount of data.


Face Recognition Face Image Feature Selection Method Face Database High Dimensional Feature Space 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    AT&T Laboratories Cambridge: The Database of Faces (1994), URL:
  2. 2.
    Graham, D.B., Allinson, N.M.: Face Recognition: From Theory to Applications. NATO ASI Series F, vol. 163, pp. 446–456. Computer and Systems Sciences (1998)Google Scholar
  3. 3.
    Belhumeur, P.N., Kriegman, D.J.: The Yale face database (1997), URL:
  4. 4.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  5. 5.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition: A literature survey. UMD CfAR Technical Report CAR-TR-948 (2000)Google Scholar
  6. 6.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  7. 7.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J.: Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3) (March 2005)Google Scholar
  8. 8.
    Serre, T., Wolf, L., Poggio, T.: Object Recognition with Features Inspired by Visual Cortex. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2005, IEEE Computer Society Press, San Diego (2005)Google Scholar
  9. 9.
    Wang, J.Z., Wiederhold, G., Firschein, O., Wei, S.X.: Content-Based Image Indexing and Searching Using Daubechies’ Wavelets. International Journal on Digital Libraries 1(4), 311–328 (1998)CrossRefGoogle Scholar
  10. 10.
    Keller, J.M., Gray, M.R., Givens, J.A.: A Fuzzy K-Nearest Neighbour Algorithm. IEEE Trans. Syst., Man, Cybern. SMC-15(4), 580–585 (1985)Google Scholar
  11. 11.
    The Center for Biological & Computational Learning (CBCL), MIT: A new biologically motivated object recognition system (Source Code) (2005), URL:

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Emre Akbaş
    • 1
  • Fatoş T. Yarman-Vural
    • 1
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

Personalised recommendations