Gender Classification of Face Images: The Role of Global and Feature-Based Information

  • Samarasena Buchala
  • Neil Davey
  • Ray J. Frank
  • Tim M. Gale
  • Martin J. Loomes
  • Wanida Kanargard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


Most computational models of gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here we use a two-way representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone.


Support Vector Machine Face Image Gender Classification Dimensionality Reduction Technique Optimal Hyperplane 
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 2004

Authors and Affiliations

  • Samarasena Buchala
    • 1
  • Neil Davey
    • 1
  • Ray J. Frank
    • 1
  • Tim M. Gale
    • 1
    • 2
  • Martin J. Loomes
    • 1
  • Wanida Kanargard
    • 1
  1. 1.Department of Computer ScienceUniversity of HertfordshireHatfieldUK
  2. 2.Department of PsychiatryQEII HospitalWelwyn Garden CityUK

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