FlowGait: Enabling Robust Long-Term Gait Recognition Across Real-World Covariates with mmWave Radar

Gait recognition enables proactive and personalized smart home interactions, but its long-term reliability is challenged by the non-static nature of gait. Covariates like carrying items and clothing induce a persistent domain shift that degrades traditional, static models. To solve this, we introduce FlowGait, a mmWave-based framework designed for robust, long-term adaptation. It combines self-training with continual learning, allowing the model to daily align with a user’s evolving gait by learning from readily available unlabeled data. It features a specialized transformer network for radar spectrogram analysis and a novel two-stage labeling algorithm that leverages the gait’s hierarchical nature to assign pseudo-labels to the unlabeled data accurately. Evaluated on three challenging datasets from 47 volunteers (covering 12 gait-covariates, 11 routes, and two weeks), FlowGait achieves high accuracies of 94.8 (cross-covariate), 98.6% (cross-route), and 95.5% (cross-day). Notably, for the long-term dataset, it reduced performance decay from 13.6% to just 1.4%, demonstrating its real-world robustness.