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Restricted Boltzmann Machines for Gender Classification

  • Jordi Mansanet
  • Alberto AlbiolEmail author
  • Roberto Paredes
  • Mauricio Villegas
  • Antonio Albiol
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

This paper deals with automatic feature learning using a generative model called Restricted Boltzmann Machine (RBM) for the problem of gender recognition in face images. The RBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the RBM in a tandem configuration.

Keywords

Representation learning RBM Gender classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jordi Mansanet
    • 1
  • Alberto Albiol
    • 1
    Email author
  • Roberto Paredes
    • 2
  • Mauricio Villegas
    • 2
  • Antonio Albiol
    • 1
  1. 1.iTEAM - Instituto de Telecomunicaciones y Aplicaciones MultimediaUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.PRHLT Research CentreUniversitat Politècnica de ValènciaValenciaSpain

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