基于分块脊波变换的手背静脉识别算法

提出一种分块提取局部方向特征，并将所有特征融合的静脉识别算法．首先，静脉图像经预处理后，利用改进的细化算法对获得的二值图像进行细化处理，得到了静脉的骨架信息；其次，将细化后的静脉图像进行分块，对分块后所有的子图像进行脊波变换，并对脊波分解系数进行主成分分析(PCA)降维，得到静脉图像的特征向量；最后，基于图像特征向量，利用支持向量机(SVM)对静脉图像进行分类匹配．试验表明，该算法获得的静脉图像特征具有较高的区分度，识别效果受图像采集和预处理过程出现的误差影响较小，正确识别率可达到97%以上．

Dorsal Hand Vein Recognition Algorithm Based on Ridgelet Transforming of Divided Blocks

A vein recognition algorithm based on fusing all local directional features which are extracted from divided blocks is proposed. Firstly, the acquired binary image is thinned by improved thinning algorithm after vein image pre processing and the vein skeleton information is obtained. Secondly, the thinned vein image is divided into blocks. Then, every sub image is processed by ridgelet transforming, the dimensions of ridgelet transforming coefficients are reduced by applying principal component analysis, and the eigenvectors of vein image are acquired. Finally, vein images are classified and matched through making use of support vector machine based on the eigenvectors of image. Experimental results show that eigenvectors which are acquired through proposed algorithm have better discrimination, recognition results are affected less by errors that are generated in image acquiring and pre processing, and the correct recognition rate exceeds 97%.