Skip to main content
SpringerLink
Log in

MASKS AND EIGENVECTORS WEIGHTS FOR EIGENFACES METHOD IMPROVEMENT

  • Chapter
Computer Vision and Graphics

Part of the book series: Computational Imaging and Vision ((CIVI,volume 32))

  • 870 Accesses

Abstract

This paper presents a possibility of improving the Eigenfaces method for face recognition by applying masks and eigenvectors weights. An idea of error function is introduced, which minimization optimizes the mask and weights and improves the recognition results.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. M. Turk, A. Pentland, Face Recognition Using Eigenfaces, in: Proceedings of Computer Vision and Pattern Recognition 1991, p.586–591.

    Google Scholar 

  2. M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, March 1991.

    Google Scholar 

  3. P. S. Penev, J. J. Atick, Local Feature Analysis: A general statistical theory for object representation, Network: Computation in Neural Systems 1996, p.477–500.

    Google Scholar 

  4. B. Moghaddam, A. Pentland, Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition, in: Face Recognition From Theory to Applications 1998, p. 230–243.

    Google Scholar 

  5. P. Grother, Face Recognition Vendor Test 2002, Supplemental Report, February 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Silesian University of Technology, Institute of Computer Science Software Division, Akademicka 16, Gliwice, Poland

    Michał Kawulok

Authors
  1. Michał Kawulok
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    K. Wojciechowski

  2. Silesian University of Technology, Gliwice, Poland

    B. Smolka

  3. Silesian University of Technology, Gliwice, Poland

    H. Palus

  4. The University of Western Australia, Crawley, Australia

    R.S. Kozera

  5. Warsaw University of Technology, Warsaw, Poland

    W. Skarbek

  6. The University of Western Australia, Crawley, Australia

    L. Noakes

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Kawulok, M. (2006). MASKS AND EIGENVECTORS WEIGHTS FOR EIGENFACES METHOD IMPROVEMENT. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_74

Download citation

  • DOI: https://doi.org/10.1007/1-4020-4179-9_74

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-4178-5

  • Online ISBN: 978-1-4020-4179-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation