摘要
为快速有效地进行手指静脉识别,针对双向二维主成分分析算法降维的特点,并对该算法进行改进,提出在经过图像预处理的手指静脉图像基础上,特征值归一化并双向加权(2D)2PCA的手指静脉识别方法((OW2D)2PCA).分析了累积特征率对(2D)2PCA的影响,以及加权值、特征值归一加权值和累积特征率对W(2D)2PCA、OW(2D)2PCA、(W2D)2PCA、(OW2D)2PCA的影响.通过建立手指静脉图像库的实验结果表明,文中提出方法能够取得较好的识别效果;对(2D)2PCA提取特征向量中的冗余信息有很强的抑制作用,双向加权比单向加权效果更好;而且(OW2D)2PCA的平均识别率高于2DPCA、(2D)2PCA、W(2D)2PCA、(W2D)2PCA和OW(2D)2PCA.
To carry out the finger vein recognition quickly and effectively, an algorithm of finger vein recognition is proposed according to the characteristics of bi-direction two-dimensional principal component analysis （（ 2D ）^2PCA） reducing the dimensions. The algorithm is bi-direction weighted （2D）^2pCA with eigenvalue normalization one （（OW2D）^2PCA） based on preprocessing image of the figure vein image. The effect of the rate of cumulate eigenvalue on （2D）^2PCA is analyzed, and the effect of the weighted value, the weighted value with eigenvalue normalization one and the rate of cumulate eigenvalue on W（2D）^2PCA,OW（2D）^2PCA, （W2D）^2PCA and （OW2D）^2PCA are analyzed as well. Experimental results on our database of finger vein images show that the presented method achieves high recognition accuracy. The redundant information of eigenvectors extracted by （2D）^2PCA is restrained strongly, and the bi-direction weighted effect is better recognition rate of （OW2D）^2PCA is higher than （W2D）^2PCA and OW（2D）^2PCA than the one direction weighted effect. The average those of 2DPCA, （2D）^2PCA, W （2D）ipcA.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2011年第3期417-424,共8页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60975022)
国家高技术研究发展计划项目(No.2008AA01Z148)
中央高校基本科研业务费专项资金(No.HEUCF100425)资助
