Fast and adaptive network of spiking neurons for multi-view visual pattern recognition

Authors:
Simei Gomes Wysoski;Lubica Benuskova;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, AUT Tech Park, 581-585 Great South Road, Ronald Trotter House, Penrose, Auckland 1051, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, AUT Tech Park, 581-585 Great South Road, Ronald Trotter House, Penrose, Auckland 1051, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, AUT Tech Park, 581-585 Great South Road, Ronald Trotter House, Penrose, Auckland 1051, New Zealand
Venue:
Neurocomputing
Year:
2008

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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.