AdaGait: Domain-Adaptive Multi-Person Gait Authentication Using Commodity WiFi Devices

WiFi channel state information (CSI) enables privacy-preserving, device-free continuous authentication on commodity hardware. However, CSI is highly sensitive to room layout and walking routes. When several people walk at the same time, their signals overlap and create strong inter-person interference. Many existing gait authentication systems degrade substantially under realistic cross-room, cross-route, and multi-person settings. As a result, we present AdaGait, a domain-adaptive multi-person gait authentication system built on a pair of WiFi devices. AdaGait targets cross-domain deployment, where training and testing differ in rooms, walking routes, and the number of persons. AdaGait first stabilizes CSI measurements via bandpass filtering, wavelet denoising, and conjugate multiplication. To better use the multi-subcarrier structure and improve sample efficiency, AdaGait constructs a subcarrier-frequency map and uses window-slicing data augmentation to expand training instances without extra data collection. For classification, AdaGait adopts a CNN-Transformer backbone together with a weakly supervised asymmetric tri-training scheme. This scheme adapts from labeled single-person source domains to weakly labeled multi-person target domains by injecting set-level label composition into pseudo-label screening. We implement AdaGait in multiple indoor rooms, walking routes, and crowd sizes. Extensive experiments show that AdaGait achieves over 90% authentication accuracy for one- and two-person cases and above 80% for three and four persons. AdaGait also consistently outperforms state-of-the-art CSI-based methods in all multi-person and cross-domain scenarios, demonstrating strong domain-adaptive performance.