Publication Details

This article was originally published as: Tivive, FHC & Bouzerdoum, A, A Shunting Inhibitory Convolutional Neural Network for Gender Classification, 18th International Conference on Pattern Recognition 2006 (ICPR 2006), 20-24 August 2006, 4, 421-424. Copyright 2006 IEEE.


Demographic features, such as gender, are very important for human recognition and can be used to enhance social and biometric applications. In this paper, we propose to use a class of convolutional neural networks for gender classification. These networks are built upon the concepts of local receptive field processing and weight sharing, which makes them more tolerant to distortions and variations in two dimensional shapes. Tested on two separate data sets, the proposed networks achieve better classification accuracy than the conventional feedforward multilayer perceptron networks. On the Feret benchmark dataset, the proposed convolutional neural networks achieve a classification rate of 97.1%.



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