标题:Face recognition system based on CNN and LBP features for classifier optimization and fusion
作者:Wu Yulin;Jiang Mingyan
作者机构:[Wu Yulin] School of Information Science and Engineering,Shandong University, Jinan, Shandong 250100, China.;[Jiang Mingyan] School of Information Sci 更多
通讯作者:Mingyan, Jiang(jiangmingyan@sdu.edu.cn)
通讯作者地址:[Mingyan, J] School of Information Science and Engineering, Shandong UniversityChina;
来源:中国邮电高校学报
出版年:2018
卷:25
期:1
页码:37-47
DOI:10.19682/j.cnki.1005-8885.2018.0004
关键词:Classifier optimization; CNN features; Face recognition; Fusion system; LBP features
摘要:Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network (CNN) and local binary pattern (LBP) can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine (SVM) optimized by artificial bee colony (ABC) algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory (ORL) and face recognition technology (FERET) databases show the superiority of the proposed approaches.
收录类别:EI;CSCD;SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048518980&doi=10.19682%2fj.cnki.1005-8885.2018.0004&partnerID=40&md5=11779aa4cd921b9083d17b036e57cfd7
TOP