GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition

TL;DR

This paper introduces GaitSADA, a novel self-aligned domain adaptation method that significantly improves mmWave radar-based gait recognition accuracy across different domains by addressing spatial and temporal shifts.

Contribution

GaitSADA is a new semi-supervised domain adaptation approach that aligns gait representations across domains using contrastive and centroid-based consistency training.

Findings

GaitSADA outperforms existing methods by 15.41% to 26.32% in accuracy.

The method effectively mitigates spatial and temporal domain shifts.

Experiments validate GaitSADA's robustness in low data regimes.

Abstract

mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41\% to 26.32\% on average accuracy in low data regimes. Code and dataset will be available at https://exitudio.github.io/GaitSADA