Gesture recognition with feature fusion using FMCW radar

Tianyang Chen*, Xichao Dong, Yaowen Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a feature fusion method for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. First, we estimate the radial information distance, Doppler and tangential information azimuth, elevation of the gesture by signal processing to construct the multi-dimensional feature data set; Then, for feature extraction and accurate classification, we design feature fusion scheme and build multi-dimensional feature convolutional neural network. Experimental results show that our proposed scheme with gesture multi-dimensional feature as input can solve the problem of insufficient feature representation in traditional Range-Doppler (RD) domain gesture recognition methods, and the recognition accuracy is improved by 4%∼8% compared with the case without feature fusion.

Original languageEnglish
Title of host publicationSeventh Asia Pacific Conference on Optics Manufacture, APCOM 2021
EditorsJiubin Tan, Xiangang Luo, Ming Huang, Lingbao Kong, Dawei Zhang
PublisherSPIE
ISBN (Electronic)9781510652088
DOIs
Publication statusPublished - 2022
Event7th Asia Pacific Conference on Optics Manufacture, APCOM 2021 - Shanghai, China
Duration: 28 Oct 202131 Oct 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12166
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference7th Asia Pacific Conference on Optics Manufacture, APCOM 2021
Country/TerritoryChina
CityShanghai
Period28/10/2131/10/21

Keywords

  • Feature fusion
  • Gesture recognition
  • Radial information
  • Tangential information

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