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.