A New Subspace Analysis Approach Based on Laplacianfaces

  • Yan Wu
  • Ren-Min Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


A new subspace analysis approach named ANLBM is proposed based on Laplacianfaces. It uses the discriminant information of training samples by supervised mechanism, enhances within-class local information by an objective function. The objective function is used to construct adjacency graph’s weight matrix. In order to avoid the drawback of Laplacianfaces’ PCA step, ANLBM uses kernel mapping. ANLBM changes the problem from minimum eigenvalue solution to maximum eigenvalue solution, reduces the redundancy of the computing and increases the precision of the result. The experiments are performed on ORL and Yale databases. Experimental results show that ANLBM has a better performance.


Training Sample Face Recognition Face Image Dimension Reduction Face Database 
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 Analysis Mach. Intel. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(22), 2323–2326 (2000)CrossRefGoogle Scholar
  4. 4.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(12), 2319–2323 (2000)CrossRefGoogle Scholar
  5. 5.
    Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Proc. Conf. Advances in Neural Information Processing System (2002)Google Scholar
  6. 6.
    He, X., Niyogi, P.: Locality Preserving Projections. In: Proc. Conf. Advances in Neural Information Processing Systems (2003)Google Scholar
  7. 7.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face Recognition Using Laplacianfaces. IEEE Trans. Pattern Analysis Mach. Intel. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  8. 8.
    He, X., Cai, D., Yan, S., Zhang, H.: Neighborhood preserving embedding. Computer Vision, ICCV 2(10), 1208–1213 (2005)Google Scholar
  9. 9.
    Cheng, J., Liu, Q., Lu, H., Chen, Y.: A supervised nonlinear local embedding for face recognition. Image Processing, ICIP 10(1), 83–86 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan Wu
    • 1
  • Ren-Min Gu
    • 1
  1. 1.Dept. of Computer Science and TechnologyTongji UniversityShanghaiP.R. of China

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