HyperGait: A Video-based Multitask Network for Gait Recognition and Human Attribute Estimation at Range and Altitude

Gait recognition is one of the mainstream approaches for identifying individuals when face information is not available. Most previous methods achieve good performance on structured indoor walking sequences with silhouettes provided. However, when these methods are applied to unconstrained outdoor sequences, a significant reduction in performance is inevitably observed due to factors such as turbulence, occlusion, view angle, and oversized clothing. To make gait recognition methods stable and effective for real-world settings, we extend gait-only-based approaches by introducing more useful biometric information such as gender, age, height, weight, and body mass index to cooperatively work with the gait recognition module. In this paper, we propose a video-based multitasking network for gait recognition and human attribute prediction at ranges of up to 1000 meters and high-pitch angles to mutually improve the robustness and accuracy of each task. Through a series of experiments on OU-MVLP and BRIAR datasets, we show that our multitasking network outperforms previous methods and provides more useful biometric information for human identification tasks.