计算机应用研究2017，Vol.34Issue(12)：3825-3827,3833,4.DOI:10.3969/j.issn.1001-3695.2017.12.069
基于选择性集成分类器的面部表情识别研究
Facial expression recognition research based on selective ensemble classifiers
摘要
Abstract
In order to improve the classification performance of facial expressions,this paper proposed a new quadratic optimization choice(QOC) ensemble classification model based on the theory of ensemble learning.Firstly,for the nine base classifiers,according to the performancc of sorting,it selected the top 30％ classifiers as candidate base classifier of ensemble model.Secondly,according to the rules of combination created ensemble model cluster.Finally,it optimized the ensemble model cluster by two times,and selected the subset of ensemble classifier with minimal generalization error.In order to verify the performance of QOC ensemble classification model,it used maximum,minimum and mean value rule as a model for comparison.The experimental results show that the relative base classifier,QOC classification model has achieved good classification results,especially for the poor sad expression recognition rate,the average recognition rate increased up to 21.11％.Compared with the non selective ensemble model,the recognition performance of the QOC ensemble classification model is also significantly improved.关键词
选择性集成学习/多分类器/面部表情识别Key words
selective ensemble learning/multiple classifiers/facial expression recognition分类
信息技术与安全科学引用本文复制引用
贾澎涛,李阳..基于选择性集成分类器的面部表情识别研究[J].计算机应用研究,2017,34(12):3825-3827,3833,4.基金项目
西安市科学计划资助项目(2017079CG/RC042(XAKD001)) （2017079CG/RC042(XAKD001）
