@inproceedings{562ec550e5244423ab984501a2c6ce9a,
title = "Gender classification using a new pyramidal neural",
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.",
author = "Phung, {S. L.} and A. Bouzerdoum",
year = "2006",
doi = "10.1007/11893257_23",
language = "English",
isbn = "3540464816",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "207--216",
booktitle = "Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings",
address = "Germany",
note = "13th International Conference on Neural Information Processing, ICONIP 2006 ; Conference date: 03-10-2006 Through 06-10-2006",
}