基于纹理特征优化LPQ的手背静脉提取方法

针对传统LPQ( Local Phase Quantization) 特征提取算法在提取手背静脉图像时存在提取细节特征不完整的问题，依据静脉纹理图像的特点将区域划分为子块分别进行LPQ 特征提取。首先，将手背静脉纹理图像分成9 个大小相等的子图像; 然后，分别采用LPQ 特征提取算法对手背静脉进行特征提取，将各子区域提取的静脉纹理信息进行整合形成整张静脉图像的向量特征，最后，使用最近邻分类器将样本进行分类实验，实验结果表明，在分块数为4 × 4 时获得最高识别率96． 50%。

Optimized LPQ Method for Extracting the Dorsal Vein of Hand

Aiming at the problem that the traditional LPQ( Local Phase Quantization) feature extraction algorithm can not extract the details of the hand vein image，the region is divided into sub-blocks for LPQ feature extraction according to the characteristics of the vein texture image． Firstly，the back vein texture image is divided into 9 equal-sized sub-images． Then，the LPQ feature extraction algorithm is used to extract the features from the dorsal vein of the opponent，and the extracted vein texture information extracted from each sub-region is integrated to form a whole vein image． The vector feature is then classified using the nearest neighbor classifier to the samples in the dataset． The experimental results show that the highest recognition rate is 96． 50% when the number of blocks is 4 × 4．