A Complete Fisher Discriminant Analysis for Based Image Matrix and Its Application to Face Biometrics

  • R. M. Mutelo
  • W. L. Woo
  • S. S. Dlay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


This paper presents a Complete Orthogonal Image discriminant (COID) method and its application to biometric face recognition. The novelty of the COID method comes from 1) the derivation of two kinds of image discriminant features, image regular and image irregular, in the feature extraction stage and 2) the development of the Complete OID (COID) features-based on the fusion of the two kinds of image discriminant features used in classification. Firstly, the COID method first derives a feature image of the face image with reduced dimensionality of the image matrix by means of two dimensional principal component analysis and then performs discriminant analysis in a double discriminant subspaces in order to derive the image regular and irregular features making it more suitable for small sample size problem. Finally combines the image regular and irregular features which are complementary for achieving better discriminant features. The feasibility of the COID method has been successfully tested using the ORL images where it was 73.8% more superior to 2DFLD method on face recognition.


Biometrics Face Recognition Fisher Discriminant Analysis (FDA) two dimensional Image Feature extraction image representation 


  1. 1.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.-y.: dimensional PCA: a new approach to appearance-based face representation and recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions 26, 131–137 (2004)CrossRefGoogle Scholar
  2. 2.
    Mutelo, R.M., Khor, L.C., Woo, W.L., Dlay, S.S.: Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition, presented at Visualization and Data Analysis 2006 (2006)Google Scholar
  3. 3.
    Li, M., Yuan, B.: 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 26, 527–532 (2005)CrossRefGoogle Scholar
  4. 4.
    Yang, J., Zhang, D., Yong, X., Yang, J.-y.: Two-dimensional discriminant transform for face recognition. Pattern Recognition 38, 1125–1129 (2005)zbMATHCrossRefGoogle Scholar
  5. 5.
    Chen, L.-F., Liao, H.-Y.M., Ko, M.-T., Lin, J.-C., Yu, G.-J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33, 1713–1726 (2000)CrossRefGoogle Scholar
  6. 6.
    Kreyszig, E.: Introductory Functional Analysis with Applications. John Wiley & Sons, Chichester (1978)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • R. M. Mutelo
    • 1
  • W. L. Woo
    • 1
  • S. S. Dlay
    • 1
  1. 1.School of Electrical, Electronic and Computer Engineering, University of Newcastle, Newcastle upon Tyne, NE1 7RUUnited Kingdom

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