Hand vein recognition based on three dimensional point clouds matching

In order to solve the problem of high false rejection rate and not supporting large data base registration in existing hand vein recognition system, a binocular stereoscopic vision device for hand vein three dimensional (3D) point can reconstruction is proposed, along with the hand vein 3D point cloud matching algorithm. The hand is lighted by an 850 nm light emitting diode (LED) light source, binocular images for 3D reconstruction are obtained by the stereo cameras. The hand vein's spatial structure is described by hand veins feature, an optimized kernel correlation analysis approach is proposed for 3D point cloud matching. Experimental results of 200 different point clouds data show the proposed system is feasible and effective, the recognition rate is 98%, false rejection rate is 2% and the false accept rate is 0%, the feature's dimension is ranged from 8000 to 12000, which is higher than that of scale invariant feature transform (SIFT). The proposed system provides a possibility for large database recognition.