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MASKS AND EIGENVECTORS WEIGHTS FOR EIGENFACES METHOD IMPROVEMENT

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Computer Vision and Graphics

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

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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.

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REFERENCES

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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
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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

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© 2006 Springer

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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

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  • 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)

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