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On the Importance of Multi-dimensional Information in Gender Estimation from Face Images

  • Juan Bekios-Calfa
  • José M. Buenaposada
  • Luis Baumela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

Estimating human face demography from images is a problem that has recently been extensively studied because of its relevant applications. We review state-of-the-art approaches to gender classification and confirm that their performance drops significantly when classifying young or elderly faces. We hypothesize that this is caused by the existence of dependencies among the demographic variables that were not considered in traditional gender classifiers. In the paper we confirm experimentally the existence of such dependencies between age and gender variables. We also prove that the performance of gender classifiers can be improved by considering the dependencies with age in a multi-dimensional approach. The performance improvement is most prominent for young and elderly faces.

Keywords

Face Image Face Detection Gender Recognition FERET Database Group Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan Bekios-Calfa
    • 1
  • José M. Buenaposada
    • 2
  • Luis Baumela
    • 3
  1. 1.Dept. de Ingeniería de Sistemas y ComputaciónUniversidad Católica del NorteAntofagastaChile
  2. 2.Dept. de Ciencias de la ComputaciónUniversidad Rey Juan CarlosMóstolesSpain
  3. 3.Dept. de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del MonteSpain

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