Chinese sign language recognition based on multi-view deep neural network for millimeter-wave radar

Abstract

People in the deaf-mute community benefit a lot from Chinese sign language (CSL) recognition, which can promote communication between sign language users and non-users. Recently, some studies have been made on sign language recognition with the millimeter-wave radar because of its advantages of non-contact measurements and privacy controls. The millimeter-wave radar acquires the motion characteristics based on the micro-Doppler images, which can be used for CSL recognition. Existing recognition methods measure the micro-Doppler image in a certain direction, which cannot reflect all the motion information of CSL and leads to the failure of recognition of the CSL with similar actions. In order to improve the recognition accuracy, this paper proposes a multi-view deep neural network (MV-DNN), which fuses micro-Doppler features measured in different directions. The simulation results show that the recognition accuracy of the proposed method reaches 96% for eight CSLs, which is 8% higher than that of the traditional single-view method.