基于局部加权表示的线性回归分类器及人脸识别  

Local Weighted Representation Based Linear Regression Classifier and Face Recognition

作　　者：杨章静 王文博[1] 黄璞 张凡龙 王昕 YANG Zhang-jing;WANG Wen-bo;HUANG Pu;ZHANG Fan-long;WANG Xin(School of Information Engineering,Nanjing Audit University,Nanjing 211815,China;Jiangsu Key Laboratory of Auditing Information Engineering,Nanjing Audit University,Nanjing 211815,China)

机构地区：[1]南京审计大学信息工程学院,南京211815 [2]南京审计大学江苏省审计信息工程重点实验室,南京211815

出　　处：《计算机科学》2021年第S02期351-359,共9页Computer Science

基　　金：国家自然科学基金(U1831127);江苏省产学研合作项目(BY2020033);江苏省高校“青蓝工程”优秀青年骨干教师培养对象。

摘　　要：线性回归分类器(Linear Regression Classifier,LRC)是一种有效的图像分类算法,然而LRC未关注数据的局部结构信息,忽略了类内样本之间的差异性,因此当人脸图像存在表情、光照、角度、遮挡等变化时分类性能不佳。针对此问题,文中提出了一种基于局部加权表示的线性回归分类器(Local WeightedRepresentation based Linear Regression Classifier,LWR-LRC)。LWR-LRC首先以测试样本与所有样本的相似性为度量,构建每类样本的加权代表样本;然后将测试样本分解为加权代表样本的线性组合;最后将测试样本分类到重构系数最大的类别。LWR-LRC考虑了样本的局部结构,构建了每类样本的最优代表样本,使用代表样本进行计算,在提高鲁棒性同时,大幅缩短了计算时间。在AR,CMU PIE,FERET和GT数据集上的实验的结果表明,LWR-LRC与NNC,SRC,LRC,CRC,MRC,LMRC等算法相比,在性能上有很强的优越性。Linear regression classifier(LRC)is an effective image classification algorithm.However,LRC does not pay attention to the local structure information of data and ignores the differences among samples within the class,and the performance may degrade when the facial images contain variations in expression,illumination,angle and occlusion.To address this problem,a linear regression classifier based on local weighted representation(LWR-LRC)is proposed.Firstly,LWR-LRC constructs a weighted representative sample for each class of samples based on the similarity between test samples and all samples,then decomposes the test samples into linear combinations of weighted representative samples,finally classifies the test samples into the category with the largest reconstruction coefficient.LWR-LRC considers the local structure of samples,constructs the optimal representative samples of each class of samples,and uses the representative samples to calculate,which improves the robustness and greatly time cost.The experiments on AR,CMU PIE,FERET and GT datasets show that LWR-LRC is superior to NNC,SRC,LRC,CRC,MRC and LMRC.

关 键 词：线性回归 流形学习 人脸识别 数据表示 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]