Gender classification using support vector machines

M. H. Yang, B. Moghaddam

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)


In this paper, Support Vector Machines (SVMs) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble-RBF networks. SVMs have also been tested with high-resolution (80-by-40 pixels) images. The difference between low and high-resolution inputs with SVMs was only 1%, thus demonstrating a degree of robustness and relative scale invariance.

Original languageEnglish
Number of pages4
Publication statusPublished - 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: 2000 Sept 102000 Sept 13


OtherInternational Conference on Image Processing (ICIP 2000)
CityVancouver, BC

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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