An RCE-based Associative Memory with Application to Human Face Recognition

Authors:
Xiaoyan Mu;Mehmet Artiklar;Paul Watta;Mohamad H. Hassoun
Affiliations:
Department of Electrical and Computer Engineering, Rose-Hulman Institute of Technology, Terre Haute, USA 47803;Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA 48202;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA 48128;Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA 48202
Venue:
Neural Processing Letters
Year:
2006

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Abstract

Many models of neural network-based associative memory have been proposed and studied. However, most of these models do not have a rejection mechanism and hence are not practical for many real-world associative memory problems. For example, in human face recognition, we are given a database of face images and the identity of each image. Given an input image, the task is to associate when appropriate the image with the corresponding name of the person in the database. However, the input image may be that of a stranger. In this case, the system should reject the input. In this paper, we propose a practical associative memory model that has a rejection mechanism. The structure of the model is based on the restricted Coulomb energy (RCE) network. The capacity of the proposed memory is desibed by two measures: the ability of the system to correctly identify known individuals, and the ability of the system to reject individuals who are not in the database. Experimental results are given which show how the performance of the system varies as the size of the database increases up to 1000 individuals.