2DPCA+2DLDA和改进的LPP相结合的人脸识别算法  

Face recognition algorithm based on 2DPCA+2DLDA and improved LPP

作　　者：李球球 杨恢先[1] 奉俊鹏 蔡勇勇[1] 翟云龙[1] 

机构地区：[1]湘潭大学材料与光电物理学院,湖南湘潭411105

出　　处：《计算机工程与应用》2015年第21期199-204,共6页Computer Engineering and Applications

基　　金：湖南省教育厅资助科研项目(No.10C1263);湘潭大学资助科研项目(No.11QDZ11)

摘　　要：针对局部保持投影(LPP)算法无监督且只保留局部信息的特性,提出一种2DPCA+2DLDA和改进的LPP相结合的人脸识别算法。将训练集样本用2DPCA+2DLDA算法进行投影,保留数据整体空间信息和分类信息;引入类内、类间信息对LPP算法的关系矩阵进行优化,使LPP成为有监督的非线性学习方法,采用改进的LPP(ILPP)算法对训练集图像进行二次投影,提取样本的局部流形信息,并作为人脸识别信息进行鉴别。在Yale和ORL人脸库的测试结果验证了该方法的有效性。Aiming at the unsupervised learning problem of Locality Preserving Projection（LPP）, which is only contained the local information, a new face recognition algorithm based on 2DPCA＋2DLDA and improved LPP is presented. Each image in the training set is mapped by 2DPCA＋2DLDA to retain the spatial information and class label information. Then, the weight matrix of LPP is optimized with the within-class and between-class label information, which makes the LPP become a supervised nonlinear learning method. Local manifolds information of training set is obtained by improved LPP（ILPP） algorithm. The experimental results on the Yale and ORL face databases validate the effectiveness of the proposed algorithm.

关 键 词：人脸识别 二维主成分分析+二维线性判别分析(2DPCA+2DLDA) 局部保持投影(LPP) 改进的局部保持投 影(1LPP) 局部流形信息 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]