Gender classification with support vector machines

Baback Moghaddam, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

175 Citations (Scopus)

Abstract

Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. The performance of SVM (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. SVM also out-performed human test subjects at the same task: in a perception study with 30 human test subjects, ranging in age from mid-20s to mid-40s, the average error rate was found to be 32% for the "thumbnails" and 6.7% with higher resolution images. The difference in performance between low- and high-resolution tests with SVM was only 1%, demonstrating robustness and relative scale invariance for visual classification.

Original languageEnglish
Title of host publicationProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
PublisherIEEE Computer Society
Pages306-311
Number of pages6
ISBN (Print)0769505805, 9780769505800
DOIs
Publication statusPublished - 2000 Jan 1
Event4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000 - Grenoble, France
Duration: 2000 Mar 282000 Mar 30

Publication series

NameProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000

Conference

Conference4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
CountryFrance
CityGrenoble
Period00/3/2800/3/30

Fingerprint

Support vector machines
Classifiers
Radial basis function networks
Optical resolving power
Image resolution
Invariance
Robustness (control systems)
Pixels

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Moghaddam, B., & Yang, M. H. (2000). Gender classification with support vector machines. In Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000 (pp. 306-311). [840651] (Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000). IEEE Computer Society. https://doi.org/10.1109/AFGR.2000.840651
Moghaddam, Baback ; Yang, Ming Hsuan. / Gender classification with support vector machines. Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000. IEEE Computer Society, 2000. pp. 306-311 (Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000).
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Moghaddam, B & Yang, MH 2000, Gender classification with support vector machines. in Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000., 840651, Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000, IEEE Computer Society, pp. 306-311, 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000, Grenoble, France, 00/3/28. https://doi.org/10.1109/AFGR.2000.840651

Gender classification with support vector machines. / Moghaddam, Baback; Yang, Ming Hsuan.

Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000. IEEE Computer Society, 2000. p. 306-311 840651 (Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Moghaddam B, Yang MH. Gender classification with support vector machines. In Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000. IEEE Computer Society. 2000. p. 306-311. 840651. (Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000). https://doi.org/10.1109/AFGR.2000.840651