Finger Vein Recognition Based on Feature Point Distance

Abstract

In recent years, finger vein recognition has been favored by more and more researchers because of its high recognition accuracy, security and convenience of collection. The rotation of the finger will reduce the recognition performance. This paper first correction the collected images through the smallest circumscribed rectangle, then extracts the region of interest according to the location of finger joints, and extracts vein features based on Niblack algorithm. Finally, the intersection points and endpoints of the veins are identified, and an modified Hausdorff distance algorithm (MHD) is used to identify. The experiment shows that the rotation average time and the extraction time of the venous feature of each picture are 8ms and 146ms, respectively. The accuracy of the non image rotation correction is 94.12%, and the accuracy of the image rotation correction is 97.21%, and the algorithm is robust to the rotation angle. It can be concluded that the algorithm has a high advantage in running speed and matching precision