Gender classification using support vector machines

M. H. Yang, B. Moghaddam

Research output: Contribution to conferencePaper

21 Citations (Scopus)

Abstract

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
Pages471-474
Number of pages4
Publication statusPublished - 2000 Dec 1
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: 2000 Sep 102000 Sep 13

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period00/9/1000/9/13

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All Science Journal Classification (ASJC) codes

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

Cite this

Yang, M. H., & Moghaddam, B. (2000). Gender classification using support vector machines. 471-474. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.