@inproceedings{ef6a245e5a9049c8a8ed3b6b155c6cb4,
title = "Static hand gesture recognition based on 2-D SAR imaging",
abstract = "Hand gesture recognition has long been a study topic in the field of Human Computer Interaction. Compared with traditional camera-based recognition systems, radar can realize dynamic hand gesture recognition at long distances and in low light conditions by exploiting the micro-Doppler (m-D) effect. However, for some static and complex hand gestures, the m-D-based recognition methods are rendered impotent. In this paper, we present a method based on 2-D synthetic aperture radar (SAR) imaging to distinguish nine kinds of static hand gestures representing the numbers 1-9. A cost-effective 77 GHz mm-wave radar is used to achieve imaging and data acquisition of different gestures. Finally, two classifiers including classic machine learning and deep learning are used to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can guarantee a promising recognition accuracy.",
keywords = "2-D synthetic aperture radar (SAR) imaging, Mm-wave radar, Static hand gesture recognition",
author = "Cuicui Yang and Zhihai Zhuo and Xingshuai Qiao and Tao Shan",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 7th Asia Pacific Conference on Optics Manufacture, APCOM 2021 ; Conference date: 28-10-2021 Through 31-10-2021",
year = "2022",
doi = "10.1117/12.2607622",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jiubin Tan and Xiangang Luo and Ming Huang and Lingbao Kong and Dawei Zhang",
booktitle = "Seventh Asia Pacific Conference on Optics Manufacture, APCOM 2021",
address = "United States",
}