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author:

Ding, Junyin (Ding, Junyin.) [1] | Xu, Zhimeng (Xu, Zhimeng.) [2] (Scholars:许志猛) | Li, Dan (Li, Dan.) [3] | Yang, Jie (Yang, Jie.) [4] | Chen, Zhizhang (Chen, Zhizhang.) [5]

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Abstract:

This paper introduces a novel approach to identity recognition using Frequency-Modulated Continuous Wave (FMCW) radar technology. A novel proposed identity recognition network (IDRVT) effectively integrates local, global, and time-frequency information for precise identification. By employing Gradient Norm Aware Minimization (GAM) during training, we achieve enhanced robustness and accuracy. Experimental results on the GID gesture dataset and IDRad gait dataset demonstrate outstanding accuracy rates of 98.29% and 96.44 %, respectively, surpassing existing methods and showcasing superior performance in identity recognition. © 2023 IEEE.

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  • [ 1 ] [Ding, Junyin]College of Physics & Information Engineering, Fuzhou University, Fuzhou, China
  • [ 2 ] [Xu, Zhimeng]College of Physics & Information Engineering, Fuzhou University, Fuzhou, China
  • [ 3 ] [Li, Dan]College of Physics & Information Engineering, Fuzhou University, Fuzhou, China
  • [ 4 ] [Yang, Jie]Fujian Post & Telecommunication, Planning & Design Institute Co. Ltd., Fuzhou, China
  • [ 5 ] [Chen, Zhizhang]Dalhousie University, Department of Electrical and Computer Engineering, Halifax; NS, Canada

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Year: 2023

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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