CLASSIFIER FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION IN FACE RECOGNITION

A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line （NFL）, which is called Center-based Nearest Neighbor （CNN）. CNN retains the advantages of NFL while it has rela- tively low computational cost. Different member classifiers are trained based on different feature spaces respectively. Corresponding mass functions can be generated based on proposed mass function de- termination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.