Component-based Gender Classification based on Hair and Facial Geometry Features

Wen-Shiung Chen, Wen-Jui Chang, Lili Hsieh, Zong-Yi Lin

Abstract

In this paper, a component-based gender classification based on hair and facial geometrical features are presented. By way of these preprocessing, hair and facial geometry features can then be extracted automatically from the face images. We compare hair detection methods by examining their color and texture features, and also analyze some geometrical features from references. The best performance of 87.15% in gender classification rate is achieved by combining the most significant hair and geometrical features which is better than some of the literature before.

References

  1. E. Makinen and R. Raisamo, “An experimental comparison of gender classification methods,” Pattern Recognition Letters, vol. 29, no. 10, pp. 1544-1556, Jul. 2008.
  2. F A. Lanitis, C. J. Taylor and T. F. Cootes, “An automatic face identification system using flexible appearance models,” 5th British Machine Vision Conference on Image and Vision Computing, vol. 13, no. 5, pp. 393- 401, 1995.
  3. B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, Aug.1996.
  4. T. Maenpaa, “Classification with color and texture: jointly or separately?” Pattern Recognition, vol. 37, no. 8, pp. 1629-1640, 2004.
Download


Paper Citation


in Harvard Style

Chen W., Chang W., Hsieh L. and Lin Z. (2012). Component-based Gender Classification based on Hair and Facial Geometry Features . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 626-630. DOI: 10.5220/0004154806260630


in Bibtex Style

@conference{ncta12,
author={Wen-Shiung Chen and Wen-Jui Chang and Lili Hsieh and Zong-Yi Lin},
title={Component-based Gender Classification based on Hair and Facial Geometry Features},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={626-630},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004154806260630},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Component-based Gender Classification based on Hair and Facial Geometry Features
SN - 978-989-8565-33-4
AU - Chen W.
AU - Chang W.
AU - Hsieh L.
AU - Lin Z.
PY - 2012
SP - 626
EP - 630
DO - 10.5220/0004154806260630