WarmGait: Thermal Array-Based Gait Recognition for Privacy-Preserving Person Re-ID
Hongbo Jiang, Lei Ye, Jingyang Hu, Xiaotian Chen, Siyu Chen, Wei Zhang, Kehua Yang
Abstract
Person re-identification (Re-ID) can recognize users based on their clothing, body shape, and other information without the need for clear facial images, and is widely applied in the field of intelligent security. Traditional Re-ID systems mainly rely on high-definition RGB cameras, but the deployment of large-scale high-definition RGB cameras indoors has caused serious privacy and ethical concerns. Recently, wireless-based Re-ID systems (Wi-Fi, RFID, millimeter-wave radar, etc.) have shown promising prospects, but the limited sensing resolution hinders their practical deployment. In this paper, we propose WarmGait, a Re-ID system based on thermal array sensors, which can achieve high-precision Re-ID at low cost and minimize the invasion of user privacy. However, using thermal arrays for Re-ID still faces two major challenges. The first is the low and unclear texture resolution of images caused by low-cost infrared devices. The second is that existing gait recognition methods require maintaining the sequential constraint of gait images, which reduces the flexibility of gait recognition or Re-ID. To address these two challenges, we first designed an edge module inspired by Taylor Finite Difference (TFD) to aggregate image edge information to help improve the resolution of infrared devices. Then, we considered gait as a collection of gait profiles and extracted features from the frame level and collection level for recognition, breaking through the limitations of the number and order of input images. After extensive experimental evaluation, our model can achieve an average recognition accuracy of 87.3% in various scenarios, demonstrating the potential of WarmGait in Re-ID.