Learning gender with support faces

Baback Moghaddam, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

503 Citations (Scopus)


Nonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET face database. The performance of SVMs (3.4 percent 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. Furthermore, the difference in classification performance with low-resolution "thumbnails" (21-by-12 pixels) and the corresponding higher resolution images (84-by-48 pixels) was found to be only 1 percent, thus demonstrating robustness and stability with respect to scale and degree of facial detail.

Original languageEnglish
Pages (from-to)707-711
Number of pages5
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number5
Publication statusPublished - 2002 May

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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