A. V. Timofeev

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This paper deals with an original method of structure parametric optimization for multimodal decision-level fusion scheme which combines the results of the partial solution for the classification task obtained from assembly of the monomodal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained. Properties of the proposed approach are proved rigorously. Suggested method has an urgent practical application in the automatic multimodal biometric person’s identification systems and in the systems for remote monitoring of extended objects. The proposed solution is easy for practical implementation into real operating systems. The paper presents a simulation study of the effectiveness of this optimized multimodal fusion classifier carried out on special bimodal biometrical database. Simulation results showed high practical effectiveness of the suggested method.

Keywords: consolidating classification decision, minimum of classification error, exponential losses function

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