An Evaluation of Fuzzy Measure for Face Recognition

In this paper, we analyze the properties and performance of the Choquet integral and fuzzy measure, particularly \(\lambda \)–fuzzy measure in the context of an aggregation of classifiers based on various facial areas. The fuzzy measure and Choquet integral have been shown to be an efficient aggregation techniques. However, in practice reported so far, the choice of the initial values of the measure corresponding to the saliency of facial features has been dependent upon the expert decision. Here, we propose an algorithmic way of finding these values. For this purpose a Particle Swarm Optimization (PSO) method is considered. The reported experimental results show that the method is more effective than the expert – centered approach.

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