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Genetic and evolutionary biometrics Exploring value preference space for hybrid feature weighting and selection

Genetic and evolutionary biometrics Exploring value preference space for hybrid feature weighting... Purpose – The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML. Design/methodology/approach – In this paper, the authors modified the evaluation function utilized by GEFeWSML such that the weights assigned to each objective (i.e. error reduction and feature reduction) were varied. For each set of weights, GEFeWSML was used to evolve FMs for the face, periocular, and face + periocular templates. The best performing FMs on the training set (FMtss) and the best performing FMs on the validation set (FM*s) were then applied to the test set in order to evaluate how well they generalized to the unseen subjects. Findings – By varying the weights assigned to each of the objectives, the authors were able to suggest values that would result in the best optimization and generalization performances for facial, periocular, and face + periocular recognition. GEFeWSML using these suggested values outperformed the previously reported GEFeWSML results, using significantly fewer features while achieving the same recognition accuracies statistically. Originality/value – In this paper, the authors investigate the relative weighting of each objective using a value preference structure and suggest the best weights to be used for each biometric modality tested. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Genetic and evolutionary biometrics Exploring value preference space for hybrid feature weighting and selection

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Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781311301490
Publisher site
See Article on Publisher Site

Abstract

Purpose – The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML. Design/methodology/approach – In this paper, the authors modified the evaluation function utilized by GEFeWSML such that the weights assigned to each objective (i.e. error reduction and feature reduction) were varied. For each set of weights, GEFeWSML was used to evolve FMs for the face, periocular, and face + periocular templates. The best performing FMs on the training set (FMtss) and the best performing FMs on the validation set (FM*s) were then applied to the test set in order to evaluate how well they generalized to the unseen subjects. Findings – By varying the weights assigned to each of the objectives, the authors were able to suggest values that would result in the best optimization and generalization performances for facial, periocular, and face + periocular recognition. GEFeWSML using these suggested values outperformed the previously reported GEFeWSML results, using significantly fewer features while achieving the same recognition accuracies statistically. Originality/value – In this paper, the authors investigate the relative weighting of each objective using a value preference structure and suggest the best weights to be used for each biometric modality tested.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Mar 22, 2013

Keywords: Biometrics; Feature selection; Feature weighting; Multiobjective optimization; Selection; Optimization techniques

References