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

Xing Wang; Chang Cui; Cong Li; Xichao Dong

doi:10.1117/12.2646268

28 October 2022 Chinese sign language recognition based on multiview deep neural network for millimeter-wave radar

Xing Wang, Chang Cui, Cong Li, Xichao Dong

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760N (2022) https://doi.org/10.1117/12.2646268
Event: SPIE Security + Defence, 2022, Berlin, Germany

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.

Citation Download Citation

Xing Wang, Chang Cui, Cong Li, and Xichao Dong "Chinese sign language recognition based on multiview deep neural network for millimeter-wave radar", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760N (28 October 2022); https://doi.org/10.1117/12.2646268

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