Radar-based human identification using deep neural network for long-term stability

• Published in: IET radar, sonar & navigation Vol. 14; no. 10; pp. 1521 - 1527
• Main Authors: Dong, Shiqi, Xia, Weijie, Li, Yi, Zhang, Qi, Tu, Dehao
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.10.2020
• Subjects: biometric technologies; biometrics (access control); continuous wave radar; CW radar; data acquisition; data privacy; deep neural network; Doppler radar; Fourier transforms; gait analysis; gait recognition; gait signatures; human body; image classification; microDoppler data; radar imaging; radar‐based human identification; radar‐based technology; recurrent neural nets; recurrent neural network; Research Article; RNN; short‐time Fourier transform; Fourier transforms; gait recognition; recurrent neural network; image classification; continuous wave radar; recurrent neural nets; CW radar; human body; radar-based technology; biometrics (access control); biometric technologies; gait analysis; gait signatures; microDoppler data; RNN; radar-based human identification; radar imaging; data acquisition; data privacy; Doppler radar; short-time Fourier transform; deep neural network
• DOI: 10.1049/iet-rsn.2019.0618
• ISSN: 1751-8784

Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have revealed that gait signatures acquired using radar can be used for human identification, but there is almost no literature on the long-term stability of gait signatures. Due to the long-term interval observation, the human micro-Doppler will change according to the subject (such as slight differences in walking posture). In this study, a novel network is proposed to realise stable identification of humans by extracting long-term stable features. The micro-Doppler data is processed by a short-time Fourier transform and finally classified by the proposed neural network. Data acquisition was carried out within more than a month. The experimental results demonstrate that the recognition accuracy of the validation set can reach about 99%, and the recognition accuracy of the test set can reach 90% (improved 3% compared with the network without a recurrent neural network), showing the potential of the proposed method in long-term stable identification.