Wavelet-Based 2-Parameter Regularized Discriminant Analysis for Face Recognition

  • Dao-Qing Dai
  • P. C. Yuen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


This paper addresses the small-size problem in Fisher Discriminant Analysis. We propose to use wavelet transform for preliminary dimensionality reduction and use a two-parameter regularization scheme for the within-class scatter matrix. The novelty of the proposed method comes from: (1) Wavelet transform with linear computation complexity is used to carry out the preliminary dimensionality reduction instead of employing a principal component analysis. The wavelet filtering also acts as smoothing out noise. (2) An optimal solution is found in the full space instead of a sub-optimal solution in a restricted subspace. (3) Detailed analysis for the contribution of the eigenvectors of the within-class scatter matrix to the overall classification performance is carried out. (4) An enhanced algorithm is developed and applied to face recognition. The recognition accuracy (rank 1) for the Olivetti database using only three images of each person as training set is 96.7859%. The experimental results show that the proposed algorithm could further improve the recognition performance.


Face Recognition Linear Discriminant Analysis Recognition Rate Wavelet Transform Face Recognition System 
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.


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  1. [1]
    P.N. Belhumeur, J.P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 19 (7) (1997), 711–720.CrossRefGoogle Scholar
  2. [2]
    R. Chellappa, C. Wilson, S. Sirohey, Human and machine recognition of faces: a survey, Proc. IEEE, Vol. 83 (5) (1995) 705–740.Google Scholar
  3. [3]
    D.Q. Dai and P.C. Yuen, Regularized discriminant analysis and its applications to face recognition, Pattern Recognition, Vol. 36 (2003), 845–847zbMATHCrossRefGoogle Scholar
  4. [4]
    I. Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conf. Series in Appl. Math., Vol. 61, SIAM Philadelphia, PA, 1992.Google Scholar
  5. [5]
    K. Etemad and R. Chellappa. Discriminant analysis for recognition of human face images. J. Opt. Soc. Am. A, 14(1997)1724–1733.CrossRefGoogle Scholar
  6. [6]
    W. J. Krzanowski, P. Jonathan, W.V. McCarthy and M.R. Thomas, Discriminant analysis with singular covariance matrices: methods and applications to spectroscopic data, Appl. Statist., Vol. 44(1995), 101–115.zbMATHCrossRefGoogle Scholar
  7. [7]
    P. J. Phillips, H. Moon, S.A. Rizvi and P. J. Rauss, The FERET evaluation methodology for face-recognition algorithms, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22(10)(2000), 1090–1104.CrossRefGoogle Scholar
  8. [8]
    L. Sirovich and M. Kirby, Application of the Karhunen-Loeve procedure for the characterization of human faces, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 12(1990), 103–108.CrossRefGoogle Scholar
  9. [9]
    D. Swets, J. Weng, Using discriminant eigenfeatures for image retrieval, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 18 (8) (1996), 831–836.CrossRefGoogle Scholar
  10. [10]
    M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3(1)(1991)72–86.CrossRefGoogle Scholar
  11. [11]
    H. Yu and J. Yang, A direct LDA algorithm for high-dimensional data-with application to face recognition, Pattern Recognition, Vol. 34(2001), 2067–2070.zbMATHCrossRefGoogle Scholar
  12. [12]
    W. Zhao, R. Chellappa and P. J. Phillips, Subspace linear discriminant analysis for face recognition, Center for Automation Research, University of Maryland, College Park, Technical Report CAR-TR-914, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Dao-Qing Dai
    • 1
  • P. C. Yuen
    • 2
  1. 1.Center for Computer Vision, Faculty of Mathematics and ComputingZhongshan UniversityGuangzhouChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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