### 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 language | English |
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Title of host publication | Proceedings of the 29th Symposium on Information Theory in the Benelux |

Subtitle of host publication | Leuven, Belgium, May 29-30, 2008 |

Editors | Liesbet van der Perre, Antoine Dejonghe, Valery Ramon |

Publisher | Werkgemeenschap voor Informatie- en Communicatietheorie (WIC) |

Pages | 27-35 |

Number of pages | 9 |

ISBN (Print) | 978-90-9023135-8 |

Publication status | Published - May 2008 |

Event | 29th Symposium on Information Theory in the Benelux 2008 - Leuven, Belgium, Leuven, Belgium Duration: 29 May 2008 → 30 May 2008 Conference number: 29 |

### Conference

Conference | 29th Symposium on Information Theory in the Benelux 2008 |
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Country | Belgium |

City | Leuven |

Period | 29/05/08 → 30/05/08 |

Other | 29-30 May 2008 |

### Keywords

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

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## 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).