A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition

DMCA

A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition (2001)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by J. Ross Beveridge , Kai She , Bruce A. Draper , Geof H. Givens

Venue:	In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Citations:	72 - 11 self

BibTeX

@INPROCEEDINGS{Beveridge01anonparametric,
    author = {J. Ross Beveridge and Kai She and Bruce A. Draper and Geof H. Givens},
    title = {A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition},
    booktitle = {In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    year = {2001},
    pages = {535--542}
}

OpenURL

Abstract

The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are compared using different choices of distance metric. Probability distributions for algoriithm recognition rates and pairwise differences in recognition rates are determined using a permutation methodology. The principal component subspace with Mahalanobis distance is the best combination; using L2 is second best. Choice of distance measure for the linear discriminant subspace matters little, and performance is always worse than the principal components classifier using either Mahalanobis or L1 distance. We make the source code for the algorithms, scoring procedures and Monte Carlo study available in the hopes others will extend this comparison to newer algorithms.

Keyphrases

principal component linear discriminant subspace recognition rate face recognition nonparametric statistical comparison different choice monte carlo study hope others probability distribution neighbor classifier distinguishable subject algoriithm recognition rate feret evaluation pairwise difference automated face recognition source code test imagery principal component subspace permutation methodology distance measure linear discriminant subspace matter mahalanobis distance l1 distance

DMCA