基于LRC与多样本扩充的指静脉识别方法北大核心CSCD

针对目前手指静脉识别由于训练样本不足引起图像识别率低的问题,提出基于线性回归分类(linear regression classification,LRC)与多样本扩充的指静脉识别方法。首先,利用矩阵变换生成原始图像的镜像,训练原始图像与镜像,增加指静脉图像中包含的有用信息;然后,基于LRC对测试和训练样本进行分类;最后,通过计算偏差得到最终分类结果,求出识别率。此外,设计了一种指静脉采集装置收集得到自建指静脉数据库。实验结果表明:所提算法在自建指静脉数据库、山东大学指静脉数据库、马来西亚理工大学指静脉数据库上的识别率分别达到98.93%、98.89%、99.67%,最低等误率为2.3888%。实验结果与其他传统和流行算法相比具有明显优势,拥有良好的实际应用价值。

Finger-vein recognition method based on LRC and multi-sample expansion

Aiming at the problem of low image recognition rate in finger-vein recognition due to insufficient training samples,a finger-vein recognition method combining linear regression classification (LRC) and multi-sample expansion is proposed.First,the matrix transformation is used to generate a mirror image of the original image,all the original images and mirror images are trained, and the useful information contained in the finger-vein image is increased.Then,the test and training samples are classified based on LRC.Finally,the final classification result is obtained by calculating the deviation,and the recognition rate is found out.In addition,a finger-vein acquisition device is designed to collect and obtain a self-built finger-vein database.The experimental results show that the recognition rate of the proposed algorithm on the finger-vein database of the self-built database,the finger-vein database of Shandong University and Malaysian University of Technology reached 98.93%,98.89% and 99.67%,and the lowest error rate was 2.388 8%.Compared with other traditional and popular algorithms,the experimental results have obvious advantages and good practical application value.