Personal authentication using hand vein and knuckle shape point cloud matching

This paper presents a new approach to authenticate individuals using hand vein point cloud matching with the help of a hardware system. The 3D point clouds of hand veins and knuckle shape were obtained by a binocular stereoscopic vision device. Edges of the hand veins and knuckle shape are used as key-points instead of other feature descriptors because they are better representing the spatial structure of hand vein patterns and significantly increasing the amount of key-points. A kernel correlation analysis approach, which has high sensitivity in hand vein spatial point clouds recognition and doesn´t need pre-training the classifier, is proposed to register the 3D point clouds. A hand vein point clouds dataset contains 18 persons´ information has been established. The experimental authentication result based on the dataset shows that the method works well and the algorithm is less time-consuming compared with 2D image matching strategies.