Journal
NEUROCOMPUTING
Volume 71, Issue 13-15, Pages 2563-2575Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2007.12.038
Keywords
spiking neural network; visual pattern recognition; face recognition; on-line classification; rank order coding
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Funding
- Tertiary Education Commission of New Zealand
- NERF [AUTX0201]
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In this paper, we describe and evaluate a new spiking neural network (SNN) architecture and its corresponding learning procedure to perform fast and adaptive multi-view visual pattern recognition. The network is composed of a simplified type of integrate-and-fire neurons arranged hierarchically in four layers of two-dimensional neuronal maps. Using a Hebbian-based training, the network adaptively changes its structure in order to respond optimally to different visual patterns. Neurons in the last layer accumulate information collected over multiple frames to reach a final decision. We tested the network with VidTimit dataset to recognize individuals using facial information from multiple frames. The experiments illustrate and evaluate the two main novelties of the network: structural adaptation and frame-by-frame accumulation of opinions. (C) 2008 Elsevier B.V. All rights reserved.
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