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 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.