Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes

  • Authors:
  • Yasmina Andreu;Pedro García-Sevilla;Ramón A. Mollineda

  • Affiliations:
  • -;-;-

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2014

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Abstract

This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA+LDA and SVM) and two performance measures (CCR and d') is provided over single- and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs.