Artículo
Feature selection for face recognition based on multi-objective evolutionary wrappers
Fecha de publicación:
10/2013
Editorial:
Elsevier
Revista:
Expert Systems With Applications
ISSN:
0957-4174
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
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Articulos(CCT - SANTA FE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Citación
Vignolo, Leandro Daniel; Milone, Diego Humberto; Scharcanski, Jacob; Feature selection for face recognition based on multi-objective evolutionary wrappers; Elsevier; Expert Systems With Applications; 40; 13; 10-2013; 5077-5084
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