Margin Maximizing Discriminant Analysis for Multi-shot Based Object Recognition

  • Hui Kong
  • Eam Khwang Teoh
  • Pengfei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


This paper discusses general object recognition by using image set in the scenario where multiple shots are available for each object. As a way of matching sets of images, canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the classical parametric distribution-based and non-parametric sample-based methods. However, it is essentially an representative but not a discriminative way for all the previous methods in using canonical correlations for comparing sets of images. Our purpose is to define a transformation such that, in the transformed space, the sum of canonical correlations (the cosine value of the principle angles between any two subspaces) of the intra-class image sets can be minimized and meantime the sum of canonical correlations of the inter-class image sets can be maximized. This is done by learning a margin-maximized linear discriminant function of the canonical correlations. Finally, this transformation is derived by a novel iterative optimization process. In this way, a discriminative way of using canonical correlations is presented. The proposed method significantly outperforms the state-of-the-art methods for two different object recognition problems on two large databases: a celebrity face database which is constructed using Image Google and the ALOI database of generic objects where hundreds of sets of images are taken at different views.


Discriminant Analysis Face Recognition Linear Discriminant Analysis Recognition Rate Face Image 
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.
    Shakhnarovich, G., Fisher, J.W., Darrel, T.: Face recognition from long-term observations. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 851–868. Springer, Heidelberg (2002)Google Scholar
  2. 2.
    Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrell, T.: Face recognition with image sets using manifold density divergence. In: Proc. of CVPR (2005)Google Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. PAMI 19, 711–720 (1997)Google Scholar
  4. 4.
    Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the Small Sample Size Problem of LDA. In: Proc. of ICPR (2002)Google Scholar
  5. 5.
    Chen, L.F., Liao, H.Y.M., Lin, J.C., Kao, M.D., Yu, G.J.: A New LDA-Based Face Recognition System which Can Solve the Small Sample Size Problem. Pattern Recognition 33, 1713–1726 (2000)CrossRefGoogle Scholar
  6. 6.
    Yu, H., Yang, J.: A Direct LDA Algorithm for High-Dimensional Data With Application to Face Recognition. Pattern Recognition 34, 2067–2070 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Satoh, S.: Comparative evaluation of face sequence matching for content-based video access. In: Proc. of FGR (2000)Google Scholar
  8. 8.
    Bressan, M., Vitria, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24, 2743–2749 (2003)CrossRefGoogle Scholar
  9. 9.
    Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proc. of FGR, pp. 318–323 (1998)Google Scholar
  10. 10.
    Wolf, L., Shashua, A.: Learning over sets using kernel principal angles. Journal of Machine Learning Research 4, 913–931 (2003)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Fukui, K., Yamaguchi, O.: Face recognition using multi-viewpoint patterns for robot vision. In: Proceedings of International Symposium of Robotics Research (2003)Google Scholar
  12. 12.
    Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–372 (1936)MATHGoogle Scholar
  13. 13.
    Bjorck, A., Golub, G.H.: Numerical methods for computing angles between linear subspaces. Mathematics of Computation 27, 579–594 (1973)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Lee, K., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. of CVPR, pp. 313–320 (2003)Google Scholar
  15. 15.
    Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. CVIU 91, 214–245 (2003)Google Scholar
  16. 16.
    Viola, P., Jones, M.: Robust real-time face detection. IJCV 57, 137–154 (2004)CrossRefGoogle Scholar
  17. 17.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. IJCV 61, 103–112 (2005)CrossRefGoogle Scholar
  18. 18.
    Oja, E.: Subspace Methods of Pattern Recognition Research Studies Press (1983)Google Scholar
  19. 19.
    Li, H., Jiang, T., Zhang, K.: Efficient and Robust Feature Extraction by Maximum Margin Criterion. In: Proc. of NIPS, vol. 16 (2004)Google Scholar
  20. 20.
    Fukunnaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1991)Google Scholar
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Kong
    • 1
  • Eam Khwang Teoh
    • 2
  • Pengfei Xu
    • 1
  1. 1.Panasonic Singapore LabsSingapore
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

Personalised recommendations