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.
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Kumari, S., Majhi, B. (2012). Gender Classification Using Artificial Neural Networks through Independent Components. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_64
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DOI: https://doi.org/10.1007/978-81-322-0487-9_64
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