Human Identification Based on Short-Time Radar Spectrograms Using Transformer Network

As an important topic in the field of public security, human identification is of great practical significance in a variety of tasks. Radar-based human identification has gained some attention in recent years, but recognizing people with short-time spectrograms remains an open problem. Since radar s...

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Bibliographic Details
Published inProceedings of the IEEE Radar Conference pp. 1516 - 1519
Main Authors Lang, Yue, Yang, Yang, Zhou, Yatong, Ji, Haoran, He, Yuan, Wang, Qing
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.12.2021
Subjects
Computational modeling
Data mining
Feature extraction
Micro-Doppler
Person Identification
Personnel
Radar
Time-frequency analysis
Transformer
Transformers
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Summary:As an important topic in the field of public security, human identification is of great practical significance in a variety of tasks. Radar-based human identification has gained some attention in recent years, but recognizing people with short-time spectrograms remains an open problem. Since radar spectrograms with short durations contain limited micro-Doppler signatures, it is hard to extract discriminative personnel information for the recognition systems, resulting in poor identification performance. In this paper, a Transformer-based network is presented to solve this problem. The proposed model utilizes the self-attention mechanism to extract the inter-frame sequential features in the time domain, which act as a supplement to the lacking frequency information in the short-time spectrograms. A dataset with 15 subjects is constructed to verify the model, and the experimental results show that the proposed Transformer-based identification strategy achieves improved accuracy than existing methods when recognizing people with short-time spectrograms.
ISSN:2640-7736
DOI:10.1109/Radar53847.2021.10028261