Recognition Method of Dorsal Hand Vein with Liveness Detection Function

In order to solve the problem that identification is easy to be counterfeited and faked, we proposed an identification method that used a near infrared camera to capture the dorsal hand veins and had a liveness detection function. The vein features in the images of dorsal hand veins provided a basis for the identification and the periodic features of the pulse waves acquired at the same time were taken as the sign of liveness detection. Specifically, a self-developed experimental setup of capturing dorsal hand veins and pulse waves was adopted to study the characteristics of the dorsal hand vein images from 70 individuals and the vein images from the living and false bodies, and an algorithm of improving the identification accuracy was proposed. Furthermore, principal component analysis was applied to reduce the dimension of the vein feature vector in the living body and simplify the classification algorithm, and Mahalanobis distance was combined to remove abnormal samples so as to improve the recognition accuracy. Then, the parameter-optimized random forests (RF) algorithm and support vector machine (SVM) algorithm were employed to achieve accurate identification of dorsal hand veins. The results show that the identification of different individuals can be performed by combining the features of dorsal hand veins with the RF and SVM algorithms. The recognition accuracy is 99.28% and 99.86%, and the recognition time is 0.368 s and 0. 110 s, respectively.