Skip to main content

A Novel Face Recognition Method

  • Conference paper
  • First Online:
Artificial Intelligence, Automated Reasoning, and Symbolic Computation (AISC 2002, Calculemus 2002)


This paper introduces a new face recognition method that treats 2D face images as 1D signals to take full advantages of wavelet multi-resolution analysis. Though there have been many applications of wavelet multi-resolution analysis to recognition tasks, the effectiveness of the approach on 2D images of varying lighting conditions, poses, and facial expressions remains to be resolved. We present a new face recognition method and the results of extensive experiments of the new method on the ORL face database, using a neural network classifier trained by randomly selected faces. We demonstrate that the method is computationally efficient and robust in dealing with variations in face images. The performance of the method also decreases gracefully with the reduction of the number of training faces.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. P. Belhumeur et al: Eigen Faces vs. Fisherfaces. Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, July 1997

    Google Scholar 

  2. C. Garcia, G. Zikos, G. Tziritas: Wavelet packet analysis for face recognition. Image and Vision Computing, 18(2000) 289–297

    Article  Google Scholar 

  3. P. McGuire and M.T.D’ Eluteriom: Eigenpaxels and a neural network approach to image classification. IEEE Tansactions on Neural Networks, VOL.12, NO.3, May 2001

    Google Scholar 

  4. Chengjun Liu, Harry Wechsler: A Shape and Texture Based Enhanced Fisher Classifier for Face Recognition. IEEE Transactions on Image Processing, VOL.10, NO.4, April 2001

    Google Scholar 

  5. A. S. Tolba and A.N. Anu-Rezq: Combined Classifiers for Invariant Face Recognition. 0-7695-0446-9/99, 1999

    Google Scholar 

  6. M. Turk, and A. Pentland: Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 1991

    Google Scholar 

  7. Olivier de Vel and Stefan Aeberhard, Line-based Face Recognition under Varying Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL.21, NO.10, October 1999

    Google Scholar 

  8. J. Zhang, Y. Yan, and M. Lades. Face Recognition: Eigenface, Elastic Matching, and Neural Nets. Proc. IEEE, VOL.85, 1997

    Google Scholar 

  9. Von der Masburg C, Pattern recognition by Labeled Graph Matching. Neural Networks, vol. 1, 141–148, 1988

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bai, L., Liu, Y. (2002). A Novel Face Recognition Method. In: Calmet, J., Benhamou, B., Caprotti, O., Henocque, L., Sorge, V. (eds) Artificial Intelligence, Automated Reasoning, and Symbolic Computation. AISC Calculemus 2002 2002. Lecture Notes in Computer Science(), vol 2385. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43865-6

  • Online ISBN: 978-3-540-45470-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics