改进的双边二维线性判别分析的手背静脉识别  

Modified Bilateral Two-Dimensional Linear Discriminant Analysis for Dorsal-Hand Vein Recognition

作　　者：王贺[1] 邓茂云[2] 姜守坤 李明明[3] 宗宇轩 刘富[1] 

机构地区：[1]吉林大学通信工程学院,长春130022 [2]西南石油大学机械电子工程学院,四川南充637001 [3]吉林大学网络中心,长春130022

出　　处：《吉林大学学报（信息科学版）》2017年第1期32-36,共5页Journal of Jilin University（Information Science Edition）

基　　金：吉林省科学技术厅基金资助项目(2014020404666GX)

摘　　要：针对双边二维线性判别分析(B2D-LDA:Bilateral Two-Dimensional Linear Discriminant Analysis)方法中多类类别均值和总体均值接近时难以分类的问题,提出了一种改进的B2D-LDA(MB2D-LDA:Modified B2D-LDA)方法,并将其运用到手背静脉特征提取中。重新定义了类间离散度矩阵,融入了每两类类间的距离,当类别均值与总体均值接近时,则用该类和其他各类类间距离组成离散度矩阵。采用基于欧氏距离的最近邻分类器进行匹配识别。结果表明,在不增加识别时间的情况下,MB2D-LDA平均识别率比B2D-LDA高2%,证明了该算法的有效性。For B2D-LDA（Bilateral Two-Dimensional Linear Discriminant Analysis）,when the class mean and the global mean are close,it is hard to classify. A new method for extracting discriminant features in dorsal hand vein recognition,termed MB2D-LDA（Modified Bilateral Two-dimensional Linear Discriminant Analysis）,is proposed. MB2D-LDA integrates the cluster information in each class by redefining the between-class scatter matrix,if the distance between the class mean and the global mean is close,the distance between the two classes incorporated into the scatter matrix. A nearest neighbor classifier for dorsal-hand vein matching based on Euclidean distance is used. Experimental results show that our presented MB2D-LDA clearly outperforms B2D-LDA,the average recognition rate of MB2D-LDA is 2% higher than that of B2D-LDA without increasing the recognition time.

关 键 词：手背静脉识别 特征提取 双边二维线性判别分析 最近邻分类器 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]