Skip to main content
Springer Nature Link
Log in

Investigating LLE Eigenface on Pose and Face Identification

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • 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)

    Article  Google Scholar 

  • Roweis, S.T., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • Saul, L.K., Roweis, S.T.: An Introduction to Locally Linear Embedding. Report at AT&T labs -Research (2000)

    Google Scholar 

  • 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 

Download references

Author information

Authors and Affiliations

  1. Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

    Shaoning Pang & Nikola Kasabov

Authors
  1. Shaoning Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikola Kasabov
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pang, S., Kasabov, N. (2006). Investigating LLE Eigenface on Pose and Face Identification. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_21

Download citation

  • DOI: https://doi.org/10.1007/11760023_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation