文献类型：期刊文献

中文题名：基于半监督LDA特征子空间优化的人脸识别算法

英文题名：A face recognition algorithm based on semi-supervised LDA feature subspace optimization

作者：纪明君[1];刘漫丹[1];才乐千[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2018

卷号：40

期号：10

起止页码：1851

中文期刊名：计算机工程与科学

外文期刊名：Computer Engineering & Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

基金：中央高校基本科研业务费专项资金(WH1213010)

语种：中文

中文关键词：PCA算法;Fisher判别分析;二进制遗传算法;特征选择;人脸识别

外文关键词：PCA algorithm;Fisher discriminant analysis;binary genetic algorithm;feature selection;face recognition

摘要：人脸特征提取是人脸识别流程最重要的步骤,特征的好坏直接影响了识别效果。为了得到更好的人脸识别效果,需要充分利用样本的信息。为了充分利用训练样本和测试样本包含的信息,提出了利用样本散度矩阵将主成分分析PCA算法和线性判别分析LDA算法加权组合的半监督LDA(SLDA)特征提取算法。同时,受组合优化问题的启发,利用二进制遗传算法对半监督特征提取算法得到的特征空间进行优化。在ORL人脸数据库上的实验结果表明:与人脸识别经典算法和部分改进算法相比,SLDA算法获得了更高的识别率。
Facial feature extraction is the most important step in face recognition process,and the quality of the feature directly affects the recognition rate.In order to get better face recognition effect,it is necessary to make full use of sample information.To make full use of the information contained in training samples and test samples,we propose a semi-supervised linear discriminant analysis(SLDA)feature extraction method based on weighted combination of principal component analysis(PCA)and linear discriminant analysis(LDA).At the same time,inspired by the combinatorial optimization problem,we use the binary genetic algorithm to optimize the feature space obtained by the semi-supervised feature extraction method.Experimental results of ORL face database show that the proposed method achieves higher recognition rate than the classical face recognition algorithm and some improved methods.