Skip to main content

Grid-Based Multi-scale PCA Method for Face Recognition in the Large Face Database

  • Conference paper
Advanced Web and Network Technologies, and Applications (APWeb 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3842))

Included in the following conference series:

Abstract

In this paper, we propose an efficient grid-based multi-scale PCA method in the large face database. This method divides the large face database into some small sub-face databases by maximizing the variance in the face sub-database and minimizing the variance between the face sub-databases, then it segments the recognition process into the local coarse profile recognition process and accurate detailed geometric sub-component analysis process, and assigns the local coarse profile recognition process to the nodes of the multimedia service grid to reduce the recognition time. Our experimental results show that with the increase of the face database, this method not only reduces the recognition time, but also remarkably increases the recognition precision, compared with other PCA methods.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of Supercomputer Applications, 15(3) (2001)

    Google Scholar 

  2. Zhang, H., Ma, H.: Virtual Semantic Resource Routing Algorithm for Multimedia Information Grid. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds.) GCC 2004. LNCS, vol. 3252, pp. 173–181. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Basu, S., Adhikari, S., et al.: mmGrid: distributed resource management infrastructure for multimedia applications. In: Parallel and Distributed Processing Symposium International Proceedings, p. 8 (2003)

    Google Scholar 

  4. Wang, X., Tang, X.: Unified Subspace Analysis for Face Recognition. In: Proc. Int’l Conf. Computer Vision, pp. 679–686 (2003)

    Google Scholar 

  5. Yunen, P.C., Lai, J.H.: Face Representation Using Independent Component Analysis. Pattern Recognition 35, 1247–1257 (2002)

    Article  Google Scholar 

  6. Kramer, M.A.: Nonlinear Principle Components Analysis Using Autoassociative Neural Networks. Am. Instit. Chemical Eng. J. 32(2), 1010 (1991)

    Google Scholar 

  7. Yang, M., Ahuja, N., Kriegman, D.: Face Recognition Using Kernel Eigenfaces. In: Proc. Int’l Conf. Image Processing, vol. 1, pp. 37–40 (2000)

    Google Scholar 

  8. Liu, Q., Huang, R., Lu, H., Ma, S.: Kernel-Based Optimized Feature Vectors Selection and Discriminant Analysis for Face Recognition. In: Proc. Int’l Conf. Pattern Recognition, pp. 362–365 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, H., Ma, H., Ming, A. (2006). Grid-Based Multi-scale PCA Method for Face Recognition in the Large Face Database. In: Shen, H.T., Li, J., Li, M., Ni, J., Wang, W. (eds) Advanced Web and Network Technologies, and Applications. APWeb 2006. Lecture Notes in Computer Science, vol 3842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11610496_144

Download citation

  • DOI: https://doi.org/10.1007/11610496_144

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31158-4

  • Online ISBN: 978-3-540-32435-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics