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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)

Abstract

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.

Keywords

Linear Discriminant Analysis Facial Image Synthetic Data Correction Algorithm Equal Error Rate 
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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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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