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

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 com...

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Veröffentlicht in:Journal of electronics (China) 2009-11, Vol.26 (6), p.771-776
Hauptverfasser: Yang, Yi, Han, Chongzhao, Han, Deqiang
Format: Artikel
Sprache:eng
Schlagworte:
Electrical Engineering
Engineering
人脸识别
分类器融合
多分类器
最近特征线
融合方法
证据理论
证据组合
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container_title Journal of electronics (China)
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creator Yang, Yi
Han, Chongzhao
Han, Deqiang
description 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.
doi_str_mv 10.1007/s11767-009-0086-3
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subjects Electrical Engineering
Engineering
人脸识别
分类器融合
多分类器
最近特征线
融合方法
证据理论
证据组合
title CLASSIFIER FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION IN FACE RECOGNITION
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