Gender Classification in Large Databases

  • Enrique Ramón-Balmaseda
  • Javier Lorenzo-Navarro
  • Modesto Castrillón-Santana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

In this paper, we address the challenge of gender classification using large databases of images with two goals. The first objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classifier that provides the best classification rate for one database, improves the classification results for other databases, that is, the cross-database performance.

Keywords

Gender Recognition Local Binary Pattern Large Facial Image Databases 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Enrique Ramón-Balmaseda
    • 1
  • Javier Lorenzo-Navarro
    • 1
  • Modesto Castrillón-Santana
    • 1
  1. 1.SIANI, Universidad de Las Palmas de Gran CanariaSpain

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