基于信息熵的局部二值模式手指静脉识别

为进一步提升手指静脉识别算法的识别率与识别速度，在图像处理阶段，提取出手指图像的感兴趣区域(region of Interest, ROI)，减少手指周围区域的干扰。为提升识别率，在局部二值模式 (local binary patterns, LBP)的基础上，引入像素邻域之间的关系，增强LBP的识别性能；然后将信息熵与改进型LBP结合得到新的ELBP特征，最后采用主成分分析(principal component analysis，PCA)降维，以减少识别时间，去除冗余特征。通过对比欧氏距离与曼哈顿距离构建的分类器，与其他主流特征比较，验证算法的识别性能。在SDUMLA数据库与天津市智能实验室采集指静脉图像数据库上，保证了算法的识别速度前提下，分别取得了99.53%、99.84%的识别率，与其他识别算法相比识别率有明显的提高。

Improved LBP Finger Vein Recognition Based on Information Entropy

In order to further improve the recognition rate and recognition speed of the finger vein recognition algorithm, in the image processing stage, the Region of Interest (ROI) of the finger image is extracted to reduce the interference of the area around the finger. In order to improve the recognition rate, the ELBP feature based on the combination of information entropy and the improved Local Binary Patterns (LBP) is proposed. Finally, Principal Component Analysis (PCA) is used to reduce the dimensionality to reduce the recognition time and remove redundant features. The classifier constructed by comparing Euclidean distance and Manhattan distance is compared with other mainstream features to verify the recognition performance of the algorithm. On the SDUMLA database and the finger vein image database collected by the Tianjin Intelligent Laboratory, the recognition rate of 99.53% and 99.84% were achieved on the premise of ensuring the recognition speed of the algorithm. Compared with others recognition algorithm, the recognition rate has been significantly improved.