A Stable Gait Recognition Algorithm Under Multiview and Multiwear Using Millimeter-Wave Radar

Currently, human gait recognition has emerged as an effective solution for person identification. Low-cost millimeter-wave radar, with its nonintrusive nature and high accuracy, can be effectively utilized in a wide range of scenarios. However, most current research primarily focuses on radar datasets with radial walking and limited samples, resulting in models with poor scalability and robustness. Therefore, this article introduces a stable gait recognition algorithm that achieves commendable results in a dataset comprising 121 individuals across eight different viewpoints and three clothing variations. The proposed model integrates ResNet18 with a multiscale temporal extraction (MSTE) structure as the backbone for feature extraction, effectively capturing gait characteristics over different time intervals. The model is optimized using a combination of gait loss, triplet loss, and center loss, significantly enhancing its stability. In the experiments, the proposed algorithm achieved an average accuracy of 87.9% and 75.0% under multiview and cross-view conditions, respectively, surpassing current state-of-the-art methods.