Eigenvalue correction results in face recognition

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

    15 Downloads (Pure)

    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
    @inproceedings{fe3280c80876491db5e5073cbe02ff64,
    title = "Eigenvalue correction results in face recognition",
    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.",
    keywords = "IR-64817, EWI-12897, SCS-Safety, METIS-251018",
    author = "Anne Hendrikse and Raymond Veldhuis and Luuk Spreeuwers",
    year = "2008",
    month = "5",
    language = "English",
    isbn = "978-90-9023135-8",
    pages = "27--35",
    editor = "{van der Perre}, Liesbet and Antoine Dejonghe and Ramon, { Valery}",
    booktitle = "Proceedings of the 29th Symposium on Information Theory in the Benelux",
    publisher = "Werkgemeenschap voor Informatie- en Communicatietheorie (WIC)",
    address = "Netherlands",

    }

    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

    TY - GEN

    T1 - Eigenvalue correction results in face recognition

    AU - Hendrikse, Anne

    AU - Veldhuis, Raymond

    AU - Spreeuwers, Luuk

    PY - 2008/5

    Y1 - 2008/5

    N2 - 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.

    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.

    KW - IR-64817

    KW - EWI-12897

    KW - SCS-Safety

    KW - METIS-251018

    M3 - Conference contribution

    SN - 978-90-9023135-8

    SP - 27

    EP - 35

    BT - Proceedings of the 29th Symposium on Information Theory in the Benelux

    A2 - van der Perre, Liesbet

    A2 - Dejonghe, Antoine

    A2 - Ramon, Valery

    PB - Werkgemeenschap voor Informatie- en Communicatietheorie (WIC)

    ER -

    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