Semi-supervised Radar-based Gait Recognition in the Wild via Ensemble Determination Strategy

Radar-based gait recognition has attracted growing interest due to radar's benefit of robust to the harsh environments, and active researchers have recently studied the feasibility of semi-supervised radar-based tasks to reduce the cost of labeling. However, in order to obtain sufficient radar data to meet the requirements of semi-supervised learning and to obtain good generalized gait recognition models, radar data need to be collected for various cases, resulting in the "independently and identically distributed (i.i.d.) assumption" in semi-supervised learning that cannot be satisfied due to the presence of "covariant perturbations". In this article, we propose a semi-supervised model for radar-based gait recognition in the wild, this model mitigates the performance degradation observed in the non-i.i.d. semi-supervised condition. The model assesses the extent to which unlabeled samples are influenced by "covariant perturbations", and then differentiates how they are incorporated in semi-supervised training, which not only prevents the samples whose semantics are destroyed from training, but also enables the model to place more emphasis on the "hard samples". We create a radar measurement dataset for the performance evaluation, and our model surpasses other seven semi-supervised learning algorithms, demonstrating the effectiveness of our model in radar-based gait recognition problem.