Eigenvalue correction results in face recognition

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Abstract

Eigenvalues of sample covariance matrices are often used in biometrics. It has been known for several decades that even though the sample covariance matrix is an unbiased estimate of the real covariance matrix [Fukunaga,1990], the eigenvalues of the sample covariance matrix are biased estimates of the real eigenvalues [Silverstein,1986]. This bias is particularly dominant when the number of samples used for estimation is in the same order as the number of dimensions, as is often the case in biometrics. We investigate the effects of this bias on error rates in verification experiments and show that eigenvalue correction can improve recognition performance.
Original languageEnglish
Title of host publicationProceedings of the 29th Symposium on Information Theory in the Benelux
Subtitle of host publicationLeuven, Belgium, May 29-30, 2008
EditorsLiesbet van der Perre, Antoine Dejonghe, Valery Ramon
PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
Pages27-35
Number of pages9
ISBN (Print)978-90-9023135-8
Publication statusPublished - May 2008
Event29th Symposium on Information Theory in the Benelux 2008 - Leuven, Belgium, Leuven, Belgium
Duration: 29 May 200830 May 2008
Conference number: 29

Conference

Conference29th Symposium on Information Theory in the Benelux 2008
CountryBelgium
CityLeuven
Period29/05/0830/05/08
Other29-30 May 2008

Fingerprint

Face Recognition
Sample Covariance Matrix
Eigenvalue
Biometrics
Estimate
Covariance matrix
Biased
Error Rate
Experiment

Keywords

  • IR-64817
  • EWI-12897
  • SCS-Safety
  • METIS-251018

Cite this

Hendrikse, A., Veldhuis, R., & Spreeuwers, L. (2008). Eigenvalue correction results in face recognition. In L. van der Perre, A. Dejonghe, & V. Ramon (Eds.), Proceedings of the 29th Symposium on Information Theory in the Benelux: Leuven, Belgium, May 29-30, 2008 (pp. 27-35). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC).
Hendrikse, Anne ; Veldhuis, Raymond ; Spreeuwers, Luuk. / Eigenvalue correction results in face recognition. Proceedings of the 29th Symposium on Information Theory in the Benelux: Leuven, Belgium, May 29-30, 2008. editor / Liesbet van der Perre ; Antoine Dejonghe ; Valery Ramon. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2008. pp. 27-35
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abstract = "Eigenvalues of sample covariance matrices are often used in biometrics. It has been known for several decades that even though the sample covariance matrix is an unbiased estimate of the real covariance matrix [Fukunaga,1990], the eigenvalues of the sample covariance matrix are biased estimates of the real eigenvalues [Silverstein,1986]. This bias is particularly dominant when the number of samples used for estimation is in the same order as the number of dimensions, as is often the case in biometrics. We investigate the effects of this bias on error rates in verification experiments and show that eigenvalue correction can improve recognition performance.",
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Hendrikse, A, Veldhuis, R & Spreeuwers, L 2008, Eigenvalue correction results in face recognition. in L van der Perre, A Dejonghe & V Ramon (eds), Proceedings of the 29th Symposium on Information Theory in the Benelux: Leuven, Belgium, May 29-30, 2008. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), pp. 27-35, 29th Symposium on Information Theory in the Benelux 2008, Leuven, Belgium, 29/05/08.

Eigenvalue correction results in face recognition. / Hendrikse, Anne; Veldhuis, Raymond; Spreeuwers, Luuk.

Proceedings of the 29th Symposium on Information Theory in the Benelux: Leuven, Belgium, May 29-30, 2008. ed. / Liesbet van der Perre; Antoine Dejonghe; Valery Ramon. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2008. p. 27-35.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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AB - Eigenvalues of sample covariance matrices are often used in biometrics. It has been known for several decades that even though the sample covariance matrix is an unbiased estimate of the real covariance matrix [Fukunaga,1990], the eigenvalues of the sample covariance matrix are biased estimates of the real eigenvalues [Silverstein,1986]. This bias is particularly dominant when the number of samples used for estimation is in the same order as the number of dimensions, as is often the case in biometrics. We investigate the effects of this bias on error rates in verification experiments and show that eigenvalue correction can improve recognition performance.

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Hendrikse A, Veldhuis R, Spreeuwers L. Eigenvalue correction results in face recognition. In van der Perre L, Dejonghe A, Ramon V, editors, Proceedings of the 29th Symposium on Information Theory in the Benelux: Leuven, Belgium, May 29-30, 2008. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). 2008. p. 27-35