Gender Classification Using Artificial Neural Networks through Independent Components

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 130)

Abstract

In this paper, an efficient technique for gender classification is developed. It uses the information maximization approach of independent component analysis for extracting the features from the face images. Further, these features were tested using back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The analysis were carried out on FERET database. The main objective of the paper is to build up an optimum classifier using neural networks. The performance of the classifier is estimated through confusion matrix and measured in terms of accuracy, sensitivity and specificity.

Keywords

ICA infomax BPNN RBFNN Accuracy Sensitivity Specificity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bartlett, M., Movellan, J., Sejnowski, T.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  2. 2.
    Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)CrossRefGoogle Scholar
  3. 3.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. IEEE (1999)Google Scholar
  4. 4.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)CrossRefGoogle Scholar
  5. 5.
    Jain, A., Huang, J.: Integrating independent components and support vector machines for gender classification, vol. 3, pp. 558–561 (2004)Google Scholar
  6. 6.
    Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)CrossRefGoogle Scholar
  7. 7.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face recognition algorithms. (a) Example 1 (b) Example 1 (c) Right ear (d) Mirroed Left earGoogle Scholar
  8. 8.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  9. 9.
    Vipsita, S., Shee, B., Rath, S.: An efficient technique for protein classification using feature extraction by artificial neural networks. In: 2010 Annual IEEE India Conference (INDICON), pp. 1–5 (December 2010) Google Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.National Institute of TechnologyRourkelaIndia

Personalised recommendations