Human Gait Recognition Based on Frontal-View Sequences Using Gait Dynamics and Deep Learning

To facilitate human gait recognition, this paper proposes a new frontal-view gait recognition method using gait dynamics and deep learning. Rather than adopting lateral-view parameters as gait features in the literature, we employ improved frontal-view features and classification methods to avoid the recognition rate drops due to the complicated surveillance environment. Specifically, we characterize the binary walking silhouettes with three different kinds of frontal-view gait features, including kinematic features, spatial ratio features and area features. In addition, we capture the gait dynamics underlying the time-varying gait features to reflect temporal dynamics information of human walking. Furthermore, we incorporate the deep feature learning information into the recognition procedure to take advantage of the deep learning technique. To obtain the optimal recognition accuracy and robustness performance against walking condition variations, we calculate the similarity between the appearing test gait dynamics and the trained gait dynamics, and propose an error-based feature fusion scheme for gait recognition. To validate the efficacy of the proposed method, we conduct experiments on published gait databases by comparing with other existing gait recognition methods.