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

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.