Robust Person Gait Identification Based on Limited Radar Measurements Using Set-Based Discriminative Subspaces Learning

Ni, Zhongfei; Huang, Binke

doi:10.1109/tim.2021.3134333

Cited by 14 publications

(2 citation statements)

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“…During Doppler radar motion measurement, the participants are not required to wear sensor devices, and there are no restrictions on the participants’ outfits. Gait-based personal identification using the micro-Doppler radar and applying deep learning to time-velocity distribution, which was calculated as the short-time Fourier transform (STFT) of the radar-received signals, has yielded high accuracy [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. It has been reported that the identification of two persons has an accuracy of 99% [ 16 ], and identification of 20 persons has an accuracy of 97% [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…It has been reported that the identification of two persons has an accuracy of 99% [ 16 ], and identification of 20 persons has an accuracy of 97% [ 17 ]. Furthermore, several person identification methods have been recently developed which are suitable for various types of realistic scenarios such as a multi-person scenario [ 18 ], use of a relatively small amount of training data [ 19 , 20 ], and scenarios which assume people walking in arbitrary directions [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning

Shioiri

Saho

2023

Sensors

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Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects’ outfits. This study explores an effective time-velocity distribution and its relevant parameters for Doppler-radar-based personal gait identification using deep learning. Most conventional studies on radar-based gait identification used a short-time Fourier transform (STFT), which is a general method to obtain time-velocity distribution for motion recognition using Doppler radar. However, the length of the window function that controls the time and velocity resolutions of the time-velocity image was empirically selected, and several other methods for calculating high-resolution time-velocity distributions were not considered. In this study, we compared four types of representative time-velocity distributions calculated from the Doppler-radar-received signals: STFT, wavelet transform, Wigner–Ville distribution, and smoothed pseudo-Wigner–Ville distribution. In addition, the identification accuracies of various parameter settings were also investigated. We observed that the optimally tuned STFT outperformed other high-resolution distributions, and a short length of the window function in the STFT process led to a reasonable accuracy; the best identification accuracy was 99% for the identification of twenty-five test subjects. These results indicate that STFT is the optimal time-velocity distribution for gait-based personal identification using the Doppler radar, although the time and velocity resolutions of the other methods were better than those of the STFT.

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