Gender classification using a new pyramidal neural network

Authors:
S. L. Phung;A. Bouzerdoum
Affiliations:
University of Wollongong, Wollongong, NSW, Australia;University of Wollongong, Wollongong, NSW, Australia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

We propose a novel neural network for classification of visual patterns. The new network, called pyramidal neural network or PyraNet, has a hierarchical structure with two types of processing layers, namely pyramidal layers and 1-D layers. The PyraNet is motivated by two concepts: the image pyramids and local receptive fields. In the new network, nonlinear 2-D are trained to perform both 2-D analysis and data reduction. In this paper, we present a fast training method for the PyraNet that is based on resilient back-propagation and weight decay, and apply the new network to classify gender from facial images.