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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 Venkatesh
In 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.

History

Event

International Conference on Optimization : Techniques and Applications (6th : 2004 : Ballarat, Vic.)

Pagination

1 - 10

Publisher

[University of Ballarat]

Location

Ballarat, Vic.

Place of publication

[Ballarat, Vic.]

Start date

2004-12-09

End date

2004-12-11

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

A Rubinov, M Sniedovich

Title of proceedings

ICOTA 2004 : 6th International Conference on Optimization : Techniques and Applications

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