Advertisement

Investigating LLE Eigenface on Pose and Face Identification

  • Shaoning Pang
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper introduces a new concept of LLE eigenface modelled by local linear embedding (LLE), and compares it with the traditional PCA eigenface from principle component analysis (PCA) on pose identity and face identity recognition through face classification. LLE eigenface is found outperforming PCA eigenface on the discrimination/recogntion of both face identity and pose identity. The superiority on face identity recognition is own to a more balanced energy distribution on LLE eigenfaces, while the superiority on pose identity recognition is due to the fact that LLE preserves a better local neighborhood of face images.

Keywords

Face Recognition Face Image Independent Component Analysis Locally Linear Embedding Face Identity 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ng, J., Gong, S.: Composite Support Vector Machines for Detection of Faces Across Views and Pose Estimation. Image and Vision Computing 20, 359–368 (2002)CrossRefGoogle Scholar
  2. Roweis, S.T., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  3. Saul, L.K., Roweis, S.T.: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research 4, 119–155 (2003)CrossRefMathSciNetGoogle Scholar
  4. Saul, L.K., Roweis, S.T.: An Introduction to Locally Linear Embedding. Report at AT&T labs -Research (2000)Google Scholar
  5. Kim, M., Kim, D., Bang, S., Lee, S.: Face Recognition Descriptor Using the Embedded HMM with the 2nd-order Block-specific Eigenvectors? ISO/IEC JTC1/SC21/WG11/M7997, Jeju (March 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shaoning Pang
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering & Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations