Learning Neighborhood Discriminative Manifolds for Video-Based Face Recognition

  • John See
  • Mohammad Faizal Ahmad Fauzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)


In this paper, we propose a new supervised Neighborhood Discriminative Manifold Projection (NDMP) method for feature extraction in video-based face recognition. The abundance of data in videos often result in highly nonlinear appearance manifolds. In order to extract good discriminative features, an optimal low-dimensional projection is learned from selected face exemplars by solving a constrained least-squares objective function based on both local neighborhood geometry and global manifold structure. The discriminative ability is enhanced through the use of intra-class and inter-class neighborhood information. Experimental results on standard video databases and comparisons with state-of-art methods demonstrate the capability of NDMP in achieving high recognition accuracy.


Manifold learning feature extraction video-based face recognition 


  1. 1.
    Brand, M.: Charting a manifold. In: Proc. of NIPS 15, pp. 961–968 (2003)Google Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosc. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman and Hall, Boca Raton (2001)zbMATHGoogle Scholar
  4. 4.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition using class specific linear projection. IEEE Trans. PAMI 19, 711–720 (1997)CrossRefGoogle Scholar
  5. 5.
    Roweis, S.T., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  6. 6.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  7. 7.
    Hadid, A., Peitikäinen, M.: From still iamge to video-based face recognition: An experimental analysis. In: IEEE FG, pp. 813–818 (2004)Google Scholar
  8. 8.
    Fan, W., Wang, Y., Tan, T.: Video-based face recognition using bayesian inference model. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 122–130. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    He, X.F., Niyogi, P.: Locality preserving projections. In: Proc. of NIPS 16, pp. 153–160 (2003)Google Scholar
  10. 10.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding: A general framework for dimensionality reduction. IEEE Transactions on PAMI 29(1), 40–51 (2007)CrossRefGoogle Scholar
  11. 11.
    He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: IEEE ICCV, pp. 1208–1213 (2005)Google Scholar
  12. 12.
    Gross, R.: Face databases. In: Li, S.Z., Jain, A.K. (eds.) Handbook of Face Recognition, Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, Chichester (2000)zbMATHGoogle Scholar
  14. 14.
    Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Visual tracking and recognition using probabilistic appearance manifolds. CVIU 99(3), 303–331 (2005)Google Scholar
  15. 15.
    Gross, R., Shi, J.: The CMU Motion of Body (MoBo) Database. Technical Report CMU-RI-TR-01-18, Robotics Institute, CMU (2001)Google Scholar
  16. 16.
    Viola, P.: Jones. M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John See
    • 1
  • Mohammad Faizal Ahmad Fauzi
    • 2
  1. 1.Faculty of Information TechnologyMultimedia University, Persiaran MultimediaCyberjayaMalaysia
  2. 2.Faculty of EngineeringMultimedia University, Persiaran MultimediaCyberjayaMalaysia

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