Gender Classification Using M-Estimator Based Radial Basis Function Neural Network

Chien-Cheng Lee

Abstract

A gender classification method using an M-estimator based radial basis function (RBF) neural network is proposed in this paper. In the proposed method, three types of effective features, including facial texture features, hair geometry features, and moustache features are extracted from a face image. Then, an improved RBF neural network based on M-estimator is proposed to classify the gender according to the extracted features. The improved RBF network uses an M-estimator to replace the traditional least-mean square (LMS) criterion to deal with the outliers in the data set. The FERET database is used to evaluate our method in the experiment. In the FERET data set, 600 images are chosen in which 300 of them are used as training data and the rest are regarded as test data. The experimental results show that the proposed method can produce a good performance.

References

  1. Alexandre, L. A., 2010. Gender recognition: A multiscale decision fusion approach. Pattern Recognition Letters, 31, 1422-1427.
  2. Moghaddam, B. and Ming-Hsuan, Y., 2000. Gender classification with support vector machines. In Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 306-311.
  3. Len, B. et al, 2011. Classification of gender and face based on gradient faces. In Proceedings of the 2011 3rd European Workshop on Visual Information Processing (EUVIP), 269-272.
  4. Ueki, K. et al., 2004. A method of gender classification by integrating facial, hairstyle, and clothing images. In Proceedings of the 17th International Conference on Pattern Recognition, 446-449.
  5. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, I-511-I-518.
  6. Stegmann, M. B. et al., 2003. FAME-a flexible appearance modeling environment. IEEE Transactions on Medical Imaging, 22, 1319-1331.
  7. Chng, E. S. et al., 1996. Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Trans. Neural Networks, 7(1), 190- 194.
  8. Leung, H. et al., 2001. Prediction of noisy chaotic time series using an optimal radial basis functions neural network. IEEE Trans. Neural Networks, 12(5), 1163- 1172.
  9. Li, C. et al., 2004. Nonlinear time series modeling and prediction using RBF network with improved clustering algorithm. in Proc. IEEE Int. Conf. Syst., Man, Cybern., 4, 3513-3518.
  10. Wang, Y. et al., 2005. Time series study of GGAP-RBF network: predictions of Nasdaq stock and nitrate contamination of drinking water. in Proc. IEEE Int. Joint Conf. Neural Networks, Montreal Canada, July 3127-3132.
  11. Huber, P. J., 1984. Robust Statistics. John Wiley and Sons, New York.
  12. Pincus, S. 1995. Approximate entropy (ApEn) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5, 110-117.
  13. Phillips, P. J. et al, 1998. The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing, 16, 295-306.
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Paper Citation


in Harvard Style

Lee C. (2014). Gender Classification Using M-Estimator Based Radial Basis Function Neural Network . In Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014) ISBN 978-989-758-046-8, pages 302-306. DOI: 10.5220/0005117103020306


in Bibtex Style

@conference{sigmap14,
author={Chien-Cheng Lee},
title={Gender Classification Using M-Estimator Based Radial Basis Function Neural Network},
booktitle={Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)},
year={2014},
pages={302-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005117103020306},
isbn={978-989-758-046-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)
TI - Gender Classification Using M-Estimator Based Radial Basis Function Neural Network
SN - 978-989-758-046-8
AU - Lee C.
PY - 2014
SP - 302
EP - 306
DO - 10.5220/0005117103020306