Wi-GR: Wi-Fi-Based Gait Recognition Using Multi-Part Velocity Profile

In recent years, with increasing user demands for convenience, privacy, and personalized experiences, gait recognition has been widely studied across various domains, such as indoor intrusion detection and smart homes. Although computer vision solutions are extensively researched for their visual intuitiveness, Wi-Fi sensing is emerging as a new research focus due to its ability to preserve privacy. However, previous studies have primarily relied on abstract features with limited interpretability or required multiple Wi-Fi links. To address these issues, we propose Wi-GR, which utilizes a Wi-Fi link to extract robust and highly interpretable gait features for user recognition. First, we construct a multi-path gait signal model to establish a clear relationship between Channel State Information (CSI) and gait motion. Then, we design a gait signal separation and enhancement method to mitigate the effects of external non-target reflections and internal multi-part reflections, which significantly impact the extraction and interpretability of gait features. Finally, fine-grained gait features that visualize gait patterns are generated using MUSIC-based and GAN-based multi-part velocity profile generation algorithms, tailored for single-person and multi-person scenarios, respectively. Numerous experiments have demonstrated that Wi-GR achieves single-person recognition accuracies of 95.3%, 94.0%, and 93.2% for 30 persons in the meeting room, corridor, and lobby, respectively, and an average accuracy of 88.3% for two-person recognition.