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A Comparison of PCA, LDA, and Matching Methods for Face Recognition  

박세제 (경희대학교 전자공학과)
박영태 (경희대학교 전자공학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.3_4, 2003 , pp. 372-378 More about this Journal
Abstract
Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.
Keywords
Face recognition; Mapping; Principal component analysis; Linear discriminant analysis; Template matching; Generalization; Computational infeasibility;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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