Improving Gender Classification Accuracy in the Wild

  • Modesto Castrillón-Santana
  • Javier Lorenzo-Navarro
  • Enrique Ramón-Balmaseda
Conference paper

DOI: 10.1007/978-3-642-41827-3_34

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)
Cite this paper as:
Castrillón-Santana M., Lorenzo-Navarro J., Ramón-Balmaseda E. (2013) Improving Gender Classification Accuracy in the Wild. In: Ruiz-Shulcloper J., Sanniti di Baja G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg

Abstract

In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Different descriptors, resolutions and classifiers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

Keywords

gender recognition local context head and shoulders LBP HOG in the wild 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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