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Hendrikse, A.J. and Veldhuis, R.N.J. and Spreeuwers, L.J.
(2008)
Eigenvalue correction results in face recognition.
In: Proceedings of the 29th Symposium on Information Theory in the Benelux, 29-30 May 2008, Leuven, Belgium.
pp. 27-35.
wic.
ISBN 978-90-9023135-8
Full text available as:
 AbstractEigenvalues 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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