International Journal of Computer Vision

, Volume 70, Issue 1, pp 91–104 | Cite as

Random Sampling for Subspace Face Recognition

  • Xiaogang Wang
  • Xiaoou Tang


Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.


random subspace method bagging LDA face recognition subspace analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Belhumeur, P.N., Hespanda, J., and Kiregeman, D. 1997. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on PAMI, 19(7):711–720.Google Scholar
  2. Breiman, L. 1996. Bagging Predictors. Machine Learning, 24(2): 123–140.zbMATHMathSciNetGoogle Scholar
  3. Chen, L., Liao, H., Ko, M., Liin, J., and Yu, G. 2000. A New LDA-based Face Recognition System Which can Solve the Samll Sample Size Problem. Pattern Recognition, 33(10): 1713–1726.CrossRefGoogle Scholar
  4. Fukunnaga, K. 1991. Introduction to Statistical Pattern Recognition. Academic Press, second edition.Google Scholar
  5. Hong, L. and Jain, A.K. 1998. Integrating Faces and Fingerprints for Personal Identification. IEEE Trans. on PAMI, 20(12):1295–1307.Google Scholar
  6. Kam Ho, T. 1999. Nearest Neighbour in Random Subspace. Intelligent Data Analysis, 3:191–209.CrossRefGoogle Scholar
  7. Kam Ho, T. 1998. The Random Subspace Method for Constructing Decision Forests. IEEE Trans. on PAMI, 20(8):832–844.Google Scholar
  8. Kegelmeyer, W.P. and Bowyer, K. 1997. Combination of Multiple Classifier Using Local Accuracy Estimates. IEEE Trans. on PAMI, 19(4):405–410.Google Scholar
  9. Kittler, J. and Roli, F. (Eds):Multiple Classifier Systems.Google Scholar
  10. Kuncheva, L.I. 2002. Switching Between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Trans. on Systems, Man and Cybernetics, Part B, 32(2).Google Scholar
  11. Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., and Duin, R.P.W. 2001. Is Independence Good for Combining Classifiers? Proc. of ICPR, 2:168–171.Google Scholar
  12. Lanitis, A., Taylor, C.J., and Cootes, T.F. 1997. Automatic Interpretation and Coding of Face Images Using Flexible Models. IEEE Trans. on PAMI, 19(7):743–756.Google Scholar
  13. Lu, X. and Jain, A.K. 2003. Resampling for Face Recognition. Proceedings of the 4th International Conference on Audio- and Video-Based Personal Authentication. Guildford, UK, pp. 869–877.Google Scholar
  14. Messer, K., Matas, J., Kittler, J., Luettin, J., and Maitre, G. 1999. XM2VTSDB: The Extended M2VTS Database. Proceedings of International Conference on Audio- and Video-Based Person Authentication, pp. 72–77.Google Scholar
  15. Moghaddam, B., Jebara, T., and Pentland, A. 2000. Bayesian Face Recognition. Pattern Recognition, 33:1771–1782.Google Scholar
  16. Monn, H. and Phillips, P.J. 1998. Analysis of PCA-Based Face Recognition Algorithms. Empirical Evaluation Techniques in Computer Vision, Bowyer, K.W. and Phillips, P.J. (eds.), IEEE Computer Society Press, Los Alamitos, CA.Google Scholar
  17. Phillips, P.J., Moon, H., Rizvi, S.A., and Rauss, P.J. 1998. The FERET Evaluation. In Face Recognition: From Theory to Applications, Wechsler, H., Phillips, P.J., Bruce, V., Soulie, F.F., and Huang, T.S. (eds.), Berlin: Springer-Verlag.Google Scholar
  18. Roli, F., Giacinto, G., and Vernazza, G. 2001. Methods for Designing Multiple Classifier Systems. In Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 78–87.Google Scholar
  19. Ross, A. and Jain, A. 2003. Information Fusion in Biometrics. Pattern Recognition Letters, 24:2115–2125.CrossRefGoogle Scholar
  20. Swets, D., Weng, J. 1996. Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. on PAMI, 16(8):831–836.Google Scholar
  21. Turk, M. and Pentland, A. 1991. Face recognition using eigenfaces. Proceedings of IEEE, CVPR, Hawaii, pp. 586–591.Google Scholar
  22. Wang, X. and Tang, X. 2005. Subspace Analysis Using Random Mixture Models. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.Google Scholar
  23. Wang, X. and Tang, X. 2004. Dual-Space Linear Discriminant Analysis for Face Recognition. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Washington, DC, USA, pp. 564–569.Google Scholar
  24. Wang, X. and Tang, X. 2004. Random Sampling LDA for Face Recognition. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 259–265,Google Scholar
  25. Wang, X. and Tang, X. 2004. A Unified Framework for Subspace Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1222–1228.MathSciNetCrossRefGoogle Scholar
  26. Wiskott, L., Fellous, J.M., Kruger, N., and von der Malsburg, C. July 1997. Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(7):775–779.CrossRefGoogle Scholar
  27. Xu, L., Krzyzak, A., and Suen, C.Y. 1992. Method of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Trans. on System, Man, and Cybernetics, 22(3):418–435.CrossRefGoogle Scholar
  28. Yacoub, S.B., Abdeljaoud, Y., and Mayoraz, E. 1999. Fusion of Face and Speech Data for Person Identity Verification. IEEE Transactions on Neural Networks, 10(5):1065–1074.CrossRefGoogle Scholar
  29. Zhao, W., Chellappa, R., Phillips, A.P.J., and Rosenfeld. 2003. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399–458.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Xiaogang Wang
    • 1
  • Xiaoou Tang
    • 1
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongShatin

Personalised recommendations