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Proceedings Paper

Comparison of different methods for gender estimation from face image of various poses
Author(s): Yohei Ishii; Hitoshi Hongo; Yoshinori Niwa; Kazuhiko Yamamoto
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Paper Abstract

Recently, gender estimation from face images has been studied for frontal facial images. However, it is difficult to obtain such facial images constantly in the case of application systems for security, surveillance and marketing research. In order to build such systems, a method is required to estimate gender from the image of various facial poses. In this paper, three different classifiers are compared in appearance-based gender estimation, which use four directional features (FDF). The classifiers are linear discriminant analysis (LDA), Support Vector Machines (SVMs) and Sparse Network of Winnows (SNoW). Face images used for experiments were obtained from 35 viewpoints. The direction of viewpoints varied ±45 degrees horizontally, ±30 degrees vertically at 15 degree intervals respectively. Although LDA showed the best performance for frontal facial images, SVM with Gaussian kernel was found the best performance (86.0%) for the facial images of 35 viewpoints. It is considered that SVM with Gaussian kernel is robust to changes in viewpoint when estimating gender from these results. Furthermore, the estimation rate was quite close to the average estimation rate at 35 viewpoints respectively. It is supposed that the methods are reasonable to estimate gender within the range of experimented viewpoints by learning face images from multiple directions by one class.

Paper Details

Date Published: 1 May 2003
PDF: 8 pages
Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515128
Show Author Affiliations
Yohei Ishii, Softopia Japan (Japan)
Hitoshi Hongo, Softopia Japan (Japan)
Yoshinori Niwa, Softopia Japan (Japan)
Kazuhiko Yamamoto, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 5132:
Sixth International Conference on Quality Control by Artificial Vision
Kenneth W. Tobin Jr.; Fabrice Meriaudeau, Editor(s)

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