%0 Journal Article
%A 吴雨林 江铭炎
%T Face recognition system based on CNN and LBP features for classifier optimization and fusion
%D 2018
%R 10.19682/j.cnki.1005-8885.2018.0004
%J 中国邮电高校学报（英文）
%P 37-47
%V 25
%N 1
%X 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 proposed approaches.
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2018.0004