Models for comparative analysis of classification methods in distributed object recognition systems

The paper considers recognition systems where classes are defined by appropriate patterns located in distributed data base. Recognition criterion is full coincidence of the presented sample with at least one of the patterns. Parallel and sequential classification methods are compared in terms of mean response time to recognition request and performance requirements. The results of numerical experiments which were carried out for multibiometric recognition systems using analytical and simulation models of queueing networks are presented.