Skip to main content
SpringerLink
Log in

Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition

  • Conference paper
Transactions on Rough Sets I

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3100))

Abstract

The paper contains description of hybrid methods of face recognition which are based on independent component analysis, principal component analysis and rough set theory. The feature extraction and pattern forming from face images have been provided using Independent Component Analysis and Principal Component Analysis. The feature selection/reduction has been realized using the rough set technique. The face recognition system was designed as rough-sets rule based classifier.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Bazan, J., Skowron, A., Synak, P.: Dynamic reducts as a tool for extracting laws from decision tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 346–355. Springer, Heidelberg (1994)

    Google Scholar 

  2. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)

    Article  Google Scholar 

  3. Cichocki, A., Bogner, R.E., Moszczynski, L.: Modified Herault-Jutten algorithms for blind separation of sources. Digital Signal Processing 7, 80–93 (1997)

    Article  Google Scholar 

  4. Cios, K., Pedrycz, W., Swiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Acad. Publ., Boston (1998)

    MATH  Google Scholar 

  5. Comon, P.: Independent component analysis - a new concept? Signal Processing 36, 287–314 (1994)

    Article  MATH  Google Scholar 

  6. Grzymała-Busse, J.W.: Knowledge acquisition under uncertainty - A rough set approach. Journal of Intelligent & Robotic Systems 1(1), 3–16

    Google Scholar 

  7. Grzymała-Busse, J.W.: LERS-a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  8. Hyvrinen, A., Oja, E.: Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Processing 64(3), 301–313 (1998)

    Article  Google Scholar 

  9. Jonsson, J., Kittler, J., Li, J.P., Matas, J.: Learning Support Vectors for Face Verification and Recognition. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 26–30 (2000)

    Google Scholar 

  10. Pawlak, Z.: Rough sets. Theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)

    MATH  Google Scholar 

  11. Samaria, F., Harter, A.: Parametrization of stochastic model for human face idntification. In: Proceedings of IEEE Workshop on Application of Computer Vision (1994), ORL database is available at http://www.cam-orl.co.uk/facedatabase.html

  12. Skowron, A.: The rough sets theory and evidence theory. Fundamenta Informaticae 13, 245–262 (1990)

    MATH  MathSciNet  Google Scholar 

  13. Swiniarski, R., Hargis, L.: Rough Sets as a Front end of Neural Networks Texture Classifiers. Neuralcomputing Journal 36, 85–102 (2001); A special issue on Rough-Neuro Computing

    Article  MATH  Google Scholar 

  14. Swiniarski, R.: An Application of Rough Sets and Haar Wavelets to Face Recognition. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 523–530. Springer, Heidelberg (2000)

    Google Scholar 

  15. Swiniarski, R., Skowron, A.: Rough Sets Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)

    Article  MATH  Google Scholar 

  16. Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Recognition and Machine Intelligence 10(9), 831–836 (1996)

    Article  Google Scholar 

  17. The FastICA MATLAB package, Available at http://www.cis.hut.fi/projects/ica/fastica/

  18. Turk, M.A., Pentland, A.P.: Face Recognition Using Eigenspaces. In: Proc. CVPR 1991, June 1991, pp. 586–591 (1991)

    Google Scholar 

  19. Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586–591 (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematical and Computer Sciences, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182, USA

    Roman W. Świniarski

  2. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Roman W. Świniarski

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

Authors
  1. Roman W. Świniarski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Skowron
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron  & Marcin S. Szczuka  & 

  3. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

  4. Multimedia Systems Department, Gdańsk University of Technology, Narutowicza 11/12, 80-952, Gdańsk, Poland

    BoĹźena Kostek

  5. Department of Mathematical and Computer Sciences, San Diego State University, 5500 Campanile Drive, CA 92182, San Diego, USA

    Roman W. Świniarski

Rights and permissions

Reprints and permissions

Copyright information

Š 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Świniarski, R.W., Skowron, A. (2004). Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds) Transactions on Rough Sets I. Lecture Notes in Computer Science, vol 3100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27794-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-27794-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22374-0

  • Online ISBN: 978-3-540-27794-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation