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

Research output: Contribution to journalArticlepeer-review

Abstract

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%.

Original languageEnglish
Pages (from-to)175-183
Number of pages9
JournalWSEAS Transactions on Systems and Control
Volume15
DOIs
StatePublished - 2020
Externally publishedYes

Bibliographical note

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

Keywords

  • 2D images
  • Databases feret
  • Databases muct
  • Facial recognition
  • Gabor wavelet
  • Kernels
  • Support vector machines

Fingerprint

Dive into the research topics of 'Efficient technique for facial image recognition with support vector machines in 2d images with cross-validation in matlab'. Together they form a unique fingerprint.

Cite this