Subject-based modular eigenspace scheme for face recognition

  • Bai-ling Zhang
  • Min-yue Fu
  • Hong Yang
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

Face recognition is an important research area with many potential applications such as biometric security. Among various techniques, eigenface method by principal component analysis (PCA) of face images has been widely used. In traditional eigenface methods, PCA was used to get the eigenvectors of the covariance matrix of a training set of face images and recognition was achieved by applying a template matching scheme with the vectors obtained by projecting new faces along a small number of eigenfaces. In order to avoid the time consuming step of recomputing eigenfaces when new faces are added, we use a set of modules to generate PCA based face representation for each subjects instead of PCA of entire face images. The localized nature of the representation makes the system easy to maintain and tolerant of local facial characteristic changes. Results indicate that the modular scheme yield accurate recognition on the widely used Olivetti Research Laboratory (ORL) face database.

Keywords

Principal Component Analysis Face Recognition Face Image Face Database Average Face 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Intrator, N., Reisfeld, D., and Yeshurun, Y.: Face recognition using hybrid supervised/unsupervised neural network. Pattern Recognition Letters 19 (1996) 67–76CrossRefGoogle Scholar
  2. 2.
    Oja, E.: Subspace methods of pattern recognition. Research Studies Press, Letchworth, U.K. 1983.Google Scholar
  3. 3.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1) (1991) 70–86Google Scholar
  4. 4.
    Pentland, A., Moghaddam, B., and Starner, T.: View-based and modular eigenspaces for face recognition. IEE Proc.-Vis. Image Signal Process. 144 (1997) 73–80CrossRefGoogle Scholar
  5. 5.
    Lawrence, S., Giles, C. L., Tsoi, A.C. and Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Networks 8 (1997) 98–113CrossRefGoogle Scholar
  6. 6.
    Lin, S.H., Kung, S.Y. and Lin, L.J.: Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. Neural Networks 8 (1997) 114–132CrossRefGoogle Scholar
  7. 7.
    Valentin, D., Abdi, H., O'Toole, A.J., and Cottrell, G.W.: Connectionist models of face processing: A survey. Pattern recognition 27(9) (1994) 1209–1230CrossRefGoogle Scholar
  8. 8.
    Hay, D.C., Young, A., and Ellis, A.W.: Routes through the face recognition system. Quarterly Journal of Experimental Psychology: Human Experimental Psychology 43 (1991) 761–791Google Scholar
  9. 9.
    Samal, A., Iyengar,P.A.: Automatic recognition and analysis of human faces and facial expressions. Pattern Recognition 25 (1992) 65–77CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Bai-ling Zhang
    • 1
  • Min-yue Fu
    • 1
  • Hong Yang
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of NewcastleNSWAustralia
  2. 2.Department of Electrical EngineeringUniversity of SydneyNSWAustralia

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