Efficient technique for facial image recognition with support vector machines in 2d images with cross-validation in matlab

Jose Augusto Cadena Moreano, Nora Bertha La Serna Palomino

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

Idioma originalInglés
Páginas (desde-hasta)175-183
Número de páginas9
PublicaciónWSEAS Transactions on Systems and Control
Volumen15
DOI
EstadoPublicada - 2020
Publicado de forma externa

Nota bibliográfica

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© 2020, World Scientific and Engineering Academy and Society. All rights reserved.

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