Rotation correction of DHV images using entropy minimization of boundary descriptor

A rotation correction method of dorsal-hand vein (DHV) images is proposed. The idea of the proposed method is to normalize all the samples to the same virtual template defined with a criterion, so that we do not need to estimate the rotation angle between a sample and every template one by one in feature matching. The proposed method contains two main procedures, i.e., boundary detection and entropy minimization. Boundary detection is used to create a boundary map, and entropy minimization is adopted to find the best rotation angle with the boundary map. Simulations and real experiments are given to show that the proposed method can be completed in 65 milliseconds and can reduce equal error rate (EER) efficiently when the rotation angle in a database is big.