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Authors: Carlos Eduardo Thomaz 1 and Gilson Antonio Giraldi 2

Affiliations: 1 Centro Universitário da FEI, Brazil ; 2 National Laboratory for Scientific Computing, Brazil

ISBN: 978-989-8111-69-2

Keyword(s): Non-linear discriminant analysis, Limited sample size problems, Face recognition.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control ; Statistical Approach

Abstract: In this paper, we extend the Maximum uncertainty Linear Discriminant Analysis (MLDA), proposed recently for limited sample size problems, to its kernel version. The new Kernel Maximum uncertainty Discriminant Analysis (KMDA) is a two-stage method composed of Kernel Principal Component Analysis (KPCA) followed by the standard MLDA. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other existing kernel discriminant methods, such as Generalized Discriminant Analysis (GDA) and Regularized Kernel Discriminant Analysis (RKDA). The classification results indicate that KMDA performs as well as GDA and RKDA, with the advantage of being a straightforward stabilization approach for the within-class scatter matrix that uses higher-order features for further classification improvements.

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Paper citation in several formats:
Eduardo Thomaz C.; Antonio Giraldi G. and (2009). A KERNEL MAXIMUM UNCERTAINTY DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 341-346. DOI: 10.5220/0001791003410346

@conference{visapp09,
author={Carlos {Eduardo Thomaz} and Gilson {Antonio Giraldi}},
title={A KERNEL MAXIMUM UNCERTAINTY DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={341-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001791003410346},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - A KERNEL MAXIMUM UNCERTAINTY DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION
SN - 978-989-8111-69-2
AU - Eduardo Thomaz, C.
AU - Antonio Giraldi, G.
PY - 2009
SP - 341
EP - 346
DO - 10.5220/0001791003410346

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