Extraction of Discriminative Manifold for Face Recognition

  • Yanmin Niu
  • Xuchu Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


It is very meaningful for dimension reduction by extraction and analysis of the underlying manifold embedded in face observation space, since the low dimensional manifold can represent the varying intrinsic features. However, this kind of manifold is perhaps not useful for face image recognition problem. This paper proposes a new discriminative manifold learning method which can efficiently discover the discriminative manifold. Besides the characteristic of preserving the local structure similarity in the face submanifold, the proposed method emphasizes the discriminative property of embedding much more throughout building and solving an object function. Experimental results on some open face datasets indicate the proposed method can achieve lower error rates.


Face Recognition Linear Discriminant Analysis Face Image Local Preserve Projection Manifold Learning 
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.


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  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)CrossRefGoogle Scholar
  5. 5.
    Mika, S., Ratsch, G., Weston, J.: Fisher discriminant analysis with kernels. In: Proc. IEEE Neural Networks for Signal Processing, USA, pp. 41–48 (1999)Google Scholar
  6. 6.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensional reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  7. 7.
    Roweis, S., Saul, L.K.: Nonlinear dimensional reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  8. 8.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 585–591. MIT Press, Cambridge (2001)Google Scholar
  9. 9.
    Yang, M.H.: Face recognition using extended isomap. ICIP (2), 117–120 (2002)Google Scholar
  10. 10.
    He, X., Niyogi, P.: Locality preserving projections. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems (NIPS), MIT Press, Cambridge (2003)Google Scholar
  11. 11.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  12. 12.
    Chen, H.T., Chang, H.W., Liu, T.L.: Local discriminant embedding and its variants. In: CVPR (2), pp. 846–853. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  13. 13.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)CrossRefGoogle Scholar
  14. 14.
    Yang, J., Yu, H., Kunz, W.: An efficient LDA algorithm for face recognition. In: 6th International Conference on Control, Automation, Robotics and Vision (ICARCV 2000), Singapore (2000)Google Scholar
  15. 15.
    Chen, L.F., Liao, H.Y.M., Ko, M.T., Lin, J.C., Yu, G.J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713–1726 (2000)CrossRefGoogle Scholar
  16. 16.
    Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the small sample size problem of LDA. In: ICPR (3), pp. 29–32 (2002)Google Scholar
  17. 17.
    AT&T Lab: AT&T face database (2002),
  18. 18.
    Yale Univ.: Face database (2002),
  19. 19.
    Sim, T., Baker, S., Bsat, M.: The CMU database. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1615–1618 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanmin Niu
    • 1
  • Xuchu Wang
    • 2
  1. 1.College of Physics and Information TechniquesChongqing Normal UniversityChongqingChina
  2. 2.Key Lab on Opto-Electronic Technique and Systems, Ministry of EducationChongqing UniversityChongqingChina

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