CSID: Enhancing Wi-Fi Based Gait Recognition via Adversarial Learning

With the development of Wi-Fi sensing, wireless-based gait recognition has become increasingly important as it supports a wide range of applications (person identification, disease diagnosis, etc.). However, two serious challenges limit the universal deployment of such Wi-Fi vision schemes: i) the limited bandwidth of Wi-Fi severely restricts the granularity of gait recognition, and ii) users’ non-gait behaviors (e.g., stopping and turning) interfere with the extraction of gait-related features. In this paper, we propose CSID, which can achieve robust gait recognition under the limited bandwidth conditions of commercial Wi-Fi devices. Specifically, we use a neural network to generate super-resolution spectrograms of channel state information (CSI), overcoming the limitation of insufficient Wi-Fi bandwidth. To overcome the challenge of non-gait behavior interference, considering the human-incomprehensible nature of Wi-Fi spectrograms, we adopt cross-domain adversarial training and further extract gait features that are independent of the interference behaviors by learning domain-independent representations. We conducted a large number of experiments in different indoor environments, and the average person identification rate of the CSID system reached 91.6%. These results demonstrate that the CSID system is promising and could be used as a complement to visual person identification systems in the future.