Gender Classification with Support Vector Machines

  title={Gender Classification with Support Vector Machines},
  author={Baback Moghaddam and Ming-Hsuan Yang},
Support Vector Machines (SVMs) are investigated for visual gender classi cation with low resolution \thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4traditional pattern classi ers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classi ers and large ensemble-RBF networks. SVMs also out-performed human test subjects at the same task: in a… CONTINUE READING
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