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

Xing Wang; Chang Cui; Cong Li; Xichao Dong

doi:10.1117/12.2661515

12 April 2023 Chinese sign language recognition based on multi-view deep neural network for millimeter-wave radar

Xing Wang, Chang Cui, Cong Li, Xichao Dong

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 1256502 (2023) https://doi.org/10.1117/12.2661515
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

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 multi-view deep neural network for millimeter-wave radar", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 1256502 (12 April 2023); https://doi.org/10.1117/12.2661515

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KEYWORDS

Radar

Convolution

Neural networks

Feature extraction

Cameras

Convolutional neural networks

Motion models

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