Gender classification using a new pyramidal neural network

Authors

Son Lam Phung, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow

RIS ID

13489

Publication Details

S. Phung & A. Bouzerdoum, "Gender classification using a new pyramidal neural network", in 13th International Conference, ICONIP 2006, 3-6 October, Hong Kong, China, Lecture Notes in Computer Science Series - Neural Information Processing, vol. 4233, pp.207-216, 2006. I. King, L. Chan, D. Wang & J. Wang

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.