Open-set gait recognition presents a critical challenge for real-world identity authentication systems, requiring simultaneous identification of known users and detection of unknown users under practical deployment conditions. However, in practical Wi-Fi sensing environments, signal noise, clothing variation, and multipath interference often blur the boundary between known and unknown classes, making traditional closed-set methods inadequate. To address this challenge, GUARD is proposed as a unified open-set and closed-set gait recognition framework based on feature reconstruction. The core idea is that known-class samples can be accurately reconstructed under matched label conditions, while unknown samples yield significantly higher reconstruction errors due to label mismatch, thereby providing a discriminative signal for open-set recognition. To enhance the stability and discriminability of features, GUARD integrates a Global Temporal Attention (GTA) mechanism to capture long-range temporal dependencies, and introduces a Pseudo-Gaussian Enhanced Self-Attention (PGESA) module that models dynamic attention distributions via Gaussian approximation, enabling selective emphasis on salient temporal features while effectively suppressing background noise. Additionally, a feature extractor locking strategy is employed to freeze identity-relevant representations once closed-set performance is optimized, preventing degradation during open-set training. Experimental results show that GUARD achieves over 20% improvement in open-set recognition rate, while maintaining approximately 95% closed-set accuracy, demonstrating superior robustness and generalization in complex sensing environments.

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are noncontact, passive, and privacy-preserving. While the existing related methods and systems have achieved good performance for person identification, they also encounter many significant challenges in practical applications. Due to the propagation properties of Wi-Fi signals, the signal at the receiver will change significantly when the user’s appearance changes. This makes single-appearance trained models unusable for cross-appearance recognition tasks. To address this challenge, we propose a deep learning-based framework for appearance-independent identification using Wi-Fi signals (WiAi-ID), the core of which lies in the fact that the domain discriminator and feature extractor are trained together in an adversarial manner, thus forcing the model to extract identity-inherent features independent of human appearance, and introduces a multiscale CNN adaptation module to capture time-span-based features. We collected Wi-Fi signal data of pedestrians with different appearances. The experimental results show that WiAi-ID can effectively eliminate the impact on identification due to pedestrian appearance variations and accordingly outperforms the current state-of-the-art video and wireless signal-based recognition methods.

Wi-Fi CSI-based gait recognition is a non-intrusive passive biometric identification technology that has garnered significant attention in the fields of security and smart furniture due to its user-friendly nature. However, in practical application scenarios, gait recognition systems face the challenge of reliably identifying subjects across different scenes or states. To overcome this challenge, this paper proposes DCS-Gait, a domain adaptation solution for cross-scene and cross-state gait recognition based on Wi-Fi CSI. DCS-Gait leverages a novel data distribution measurement called Cross-Attention Metric to align the class-level data distribution differences, enabling the model to learn invariant features across scenes and states. To address the issue of data annotation, we employ a pre-training method to obtain pseudo labels for the dataset. Additionally, a combined matching filtering technique is utilized to generate high-quality pseudo labels for unrecognized data, which can be further employed for supervised model training. We evaluated the effectiveness of DCS-Gait on a large test set consisting of 34 subjects, 2 scenes, and 3 different states, and the results demonstrate significant improvements over the state-of-the-art baselines in both cross-scene and cross-state gait recognition tasks. DCS-Gait provides a promising and reliable solution for accurate cross-scene and cross-state gait recognition in real-world settings.