One-Shot Gait Recognition Under Arbitrary Trajectories Using a Single WiFi Link

Gait recognition serves as a foundation for numerous applications, ranging from early disease diagnosis to continuous user authentication. While traditional methods rely on cameras or wearables to achieve gait recognition, RF-based solutions have emerged in recent years that better preserve privacy and do not require dedicated wearables or sensors. Among the RF signals explored, WiFi-based solutions are particularly promising due to the ubiquitous deployment of WiFi infrastructure. However, existing WiFi-based methods either require multiple WiFi links or constrain users to walk along predefined trajectories. In addition, they typically require extensive data collection to train the recognition model. These limitations significantly impede the practical deployment and broader adoption of WiFi-based gait recognition systems. In this work, we present WiSiGait, a one-shot gait recognition framework that operates using only a single WiFi link and does not require users to follow any predefined trajectories. To realize WiSiGait, we extract trajectory-independent gait features that can be captured using a single WiFi link. For identification, we explore Siamese neural networks to realize one-shot gait recognition, eliminating the need for extensive pre-training data collection. Extensive experiments demonstrate that our system achieves robust gait recognition across arbitrary trajectories using only a single WiFi link. We believe this work represents a significant step toward the practical deployment of ubiquitous gait recognition. A demo video is available at: https://youtu.be/eTOkG5O3CCE.