基于核主元分析和局部保持投影的手背静脉识别

为了保持手背静脉空间的局部结构,运用局部保持投影(LPP)方法进行手背静脉识别.但是对于小样本图像识别,LPP中的特征方程矩阵通常存在奇异性.为了解决这个问题,提出首先利用核主元分析(KPCA)降低手背静脉空间的维数,再对低维图像应用LPP提取局部特征.对已有手背静脉图像库进行测试,实验结果表明,与传统的PCA和PCA+LPP相比,该方法大大提高了系统的识别率,而且特征提取时间为2.6 s,满足实时系统的要求.

Palm-Dorsa Vein Recognition Based on Kernel Principal Component Analysis and Locality Preserving Projection Methods

In order to preserve the local structure of the palm-dorsa vein space,locality preserving projection(LPP) was applied to palm-dorsa vein recognition.In small-sized sample cases such as image recognition,the matrix of the eigenvalue equation is usually singular.To solve the problem,kernal principal component analysis(KPCA) method was presented to reduce the palm-dorsa vein space dimensions.Then LPP was used to extract the local features.The algorithm was tested in the existing palm-dorsa vein database.The results showed that the new method has much higher recognition rate and the feature extraction time is 2.6 s,so it satisfies the real-time system specifications.