Mixture-of-Laplacian Faces and Its Application to Face Recognition

  • S. Noushath
  • Ashok Rao
  • G. Hemantha Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


The locality preserving projection (LPP), known as Laplacianfaces, was recently proposed as a transformation technique of mapping which optimally preserves the neighborhood structure of the dataset. In this paper, an efficient method for face recognition called mixture-of-Laplacianfaces (or LPP mixture model) is proposed, which obtains several sets of Laplacianfaces through Expectation-Maximization (EM) learning of Gaussian Mixture Models (GMM). Experiments carried out by using this on ORL, FERET and COIL-20 indicate superior performance as compared with method based on Laplacianfaces and other contemporary subspace methods.


Training Sample Face Recognition Gaussian Mixture Model Training Image Recognition Accuracy 
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.


  1. 1.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  2. 2.
    Chen, W., Er, M.J., Wu, S.: PCA and LDA in DCT domain. Pattern Recognition Letters 26, 2471–2482 (2005)Google Scholar
  3. 3.
    Chien, Chen.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1644–1649 (2002)CrossRefGoogle Scholar
  4. 4.
    Murase, H., Nayar, S.K.: Visual learning and recognition of 3D objects from appearance. International Journal of Computer Vision 14(1), 5–24 (1995)CrossRefGoogle Scholar
  5. 5.
    Kim, H.C., Kim, D., Bang, S.Y.: Face recognition using the mixture-of-eigenfaces method. Pattern Recognition Letters 23, 1549–1558 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  7. 7.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transactions on Pattern Analalysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  8. 8.
    Wu, J., Zhou, Z.-H.: Face recognition with one training image per person. Pattern Recognition Letters 23, 1711–1719 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    He, X., Yan, S., Hu, Y., Niyogi, P.: Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)CrossRefGoogle Scholar
  10. 10.
    Yin, H., Fu, P., Meng, S.: Sampled FLDA for face recognition with single training image per person. Neurocomputing 69(16-18), 2443–2445 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • S. Noushath
    • 1
  • Ashok Rao
    • 2
  • G. Hemantha Kumar
    • 1
  1. 1.Dept of Studies in Computer Science,University of Mysore, Mysore - 570 006India
  2. 2.Dept of Electronics and Communication, SJ College of Engineering, Mysore - 570 006India

Personalised recommendations