Empirical performance analysis of linear discriminant classifiers

In face recognition literature, holistic template matching systems and geometrical local feature based systems have been pursued. In the holistic approach, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are popular ones. More recently, the combination of PCA and LDA has be...

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Bibliographic Details
Published inProceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231) pp. 164 - 169
Main Authors Zhao, W., Chellappa, R., Nandhakumar, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Automation
Bayesian methods
Degradation
Density functional theory
Educational institutions
Face recognition
Linear discriminant analysis
Pattern recognition
Performance analysis
Principal component analysis
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Summary:In face recognition literature, holistic template matching systems and geometrical local feature based systems have been pursued. In the holistic approach, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are popular ones. More recently, the combination of PCA and LDA has been proposed as a superior alternative over pure PCA and LDA. In this paper, we illustrate the rationales behind these methods and the pros and cons of applying them to pattern classification task. A theoretical performance analysis of LDA suggests applying LDA over the principal components from the original signal space or the subspace. The improved performance of this combined approach is demonstrated through experiments conducted on both simulated data and real data.
ISBN:0818684976
9780818684975
ISSN:1063-6919
DOI:10.1109/CVPR.1998.698604