Face recognition system based on CNN and LBP features for classifier optimization and fusion

作     者：Wu Yulin Jiang Mingyan 

作者机构：School of Information Science and EngineeringShandong University 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2018年第25卷第1期

页      面：37-47页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Natural Science Foundation of Shandong Province ( ZR2014FM039) the National Natural Science Foundation of China ( 61771293) 

主　　题：CNN features LBP features classifier optimization fusion system face recognition 

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.