  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
