Cross-device hand vein recognition based on improved SIFT

Abstract

The recognition rate of SIFT algorithm in single hand vein database has been as high as 99.5%. But with the development of Internet-plus technology, the demand for distributed systems becomes more and more significant. However, the problem of picture quality caused by cross-device makes the intraclass variations larger. For example, when gathering the dorsal hand vein images, subtle changes in relative distance and orientation among the imaging camera, the illumination LED arrays and the different location of users’ hand, as well as shielding by the external housing box from ambient light sources and so on, these will make large difference to one person’s hand images. So, including the contrast, the lightness, the shifting, the angle of rotation, the size and so on, these differences make it possible to use some traditional methods to recognize dorsal hand vein with a low recognition rate of less than 50%. Therefore, based on the traditional SIFT, this paper optimized the scale factor [Formula: see text], extreme searching neighborhood structure and matching threshold [Formula: see text]. It can be seen that the cross-device hand vein feature is more robust, and the recognition rate reached an average of 88.5%.