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Gender Classification of Faces Using Adaboost

  • Rodrigo Verschae
  • Javier Ruiz-del-Solar
  • Mauricio Correa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gender classification. We built several gender classification systems under the proposed framework using different features (LBP, wavelets, rectangular, etc.). These systems are analyzed and evaluated using standard face databases (FERET and BioID), and a new gender classification database of real-world images.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rodrigo Verschae
    • 1
    • 2
    • 3
  • Javier Ruiz-del-Solar
    • 1
    • 2
  • Mauricio Correa
    • 1
    • 2
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile
  2. 2.Center for Web Research, Department of Computer ScienceUniversidad de ChileChile
  3. 3.CMLAENS CachanFrance

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