Article ID Journal Published Year Pages File Type
409070 Neurocomputing 2008 13 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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