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Face Recognition by Regularized Discriminant Analysis
| Citations: | 9 - 0 self |
BibTeX
@MISC{Dai_facerecognition,
author = {Dao-qing Dai and Pong C. Yuen},
title = {Face Recognition by Regularized Discriminant Analysis},
year = {}
}
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Abstract
Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database. Index Terms—Face recognition, optimization, regularized discriminant analysis (RDA), small sample-size problem. I.
Keyphrases
face recognition regularized discriminant analysis small sample-size problem face recognition community feature dimension yale database linear discriminant analysis discriminant analysis face recognition system feret database new regularization scheme fisher index great concern olivetti research laboratory database index term face recognition
