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Automatic parameter selection for Eigenfaces
conference contribution
posted on 2004-01-01, 00:00 authored by R Tjahyadi, W Liu, Svetha VenkateshSvetha VenkateshIn this paper, we investigate the parameter selection issues for Eigenfaces. Our focus is on the eigenvectors and threshold selection issues. We propose a systematic approach in selecting the eigenvectors based on the relative errors of the eigenvalues. In addition, we have designed a method for selecting the classification threshold that utilizes the information obtained from the training database effectively. Experimentation was conducted on the ORL and AMP face databases with results indicating that the automatic eigenvectors and threshold selection methods provide an optimum recognition in terms of precision and recall rates. Furthermore, we show that the eigenvector selection method outperforms energy and stretching dimension methods in terms of selected number of eigenvectors and computation cost.
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Event
International Conference on Optimization : Techniques and Applications (6th : 2004 : Ballarat, Vic.)Pagination
1 - 10Publisher
[University of Ballarat]Location
Ballarat, Vic.Place of publication
[Ballarat, Vic.]Start date
2004-12-09End date
2004-12-11Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
A Rubinov, M SniedovichTitle of proceedings
ICOTA 2004 : 6th International Conference on Optimization : Techniques and ApplicationsUsage metrics
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