Analysis of Eigenvalue Correction Applied to Biometrics

  • Anne Hendrikse
  • Raymond Veldhuis
  • Luuk Spreeuwers
  • Asker Bazen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Eigenvalue estimation plays an important role in biometrics. However, if the number of samples is limited, estimates are significantly biased. In this article we analyse the influence of this bias on the error rates of PCA/LDA based verification systems, using both synthetic data with realistic parameters and real biometric data. Results of bias correction in the verification systems differ considerable between synthetic data and real data: while the bias is responsible for a large part of classification errors in the synthetic facial data, compensation of the bias in real facial data leads only to marginal improvements.


Linear Discriminant Analysis Facial Image Synthetic Data Correction Algorithm Equal Error Rate 
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  1. 1.
    Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn. Academic Press Professional, Inc., San Diego (1990)Google Scholar
  2. 2.
    Girko, V.: Theory of Random Determinants. Kluwer, Dordrecht (1990)Google Scholar
  3. 3.
    Muirhead, R.J.: Aspects of multivariate statistical theory. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, Inc., Chichester (1982)Google Scholar
  4. 4.
    Stein, C.: Lectures on the theory of estimation of many parameters. Journal of Mathematical Sciences 34(1), 1371–1403 (1986)Google Scholar
  5. 5.
    El Karoui, N.: Spectrum estimation for large dimensional covariance matrices using random matrix theory. ArXiv Mathematics e-prints (September 2006)Google Scholar
  6. 6.
    Silverstein, J.W.: Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices. J. Multivar. Anal. 55(2), 331–339 (1995)Google Scholar
  7. 7.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)Google Scholar
  8. 8.
    Hendrikse, A.J., Spreeuwers, L.J., Veldhuis, R.N.J.: Eigenvalue correction results in face recognition. In: Twenty-ninth Symposium on Information Theory in the Benelux, pp. 27–35 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anne Hendrikse
    • 1
  • Raymond Veldhuis
    • 1
  • Luuk Spreeuwers
    • 1
  • Asker Bazen
    • 2
  1. 1.Fac. EEMCS, Signals ans Systems GroupUnversity of TwenteEnschedeThe Netherlands
  2. 2.Uniqkey BiometricsThe Netherlands

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