Human Gait Recognition Based on Deterministic Learning and Data Stream of Microsoft Kinect

Abstract

Gait is an important biometric technology for human identification at a distance. This study focuses on gait features obtained by Microsoft Kinect and proposes a new model-based gait recognition method by combining deterministic learning theory and the data stream of Kinect. Deterministic learning theory is employed to capture the gait dynamics underlying Kinect-based gait parameters. Spatial-temporal gait features can be represented as the gait dynamics underlying the trajectories of spatial-temporal parameters, which can implicitly reflect the temporal changes of silhouette shape. Kinematic gait features can be represented as the gait dynamics underlying the trajectories of kinematic parameters, which can represent the temporal changes of body structure and dynamics. Both spatial-temporal and kinematic cues can be used separately for gait recognition using the smallest error principle. They are fused on the decision level to improve the gait recognition performance. Additionally, we discuss how to eliminate the effect of view angle on the proposed method. The experimental results indicate that encouraging recognition accuracy can be achieved.