The existing gait recognition methods based on silhouettes are easy to be interfered by clothing and other external conditions. Although the 3 D model-based recognition method resists external interference to a certain extent

it has additional requirements of camera equipment and complicated model calculations. In order to solve the above problems

" light" model for pedestrian motion is established by using 3 D pose estimation technology.The spatiotemporal information of pedestrian 3 D spatial motion is extracted by using neural network framework

and the information is fused with the information of skeleton map to further enrich the gait features. The results on CASIA-B dataset show that the fusion of 3 D spatiotemporal motion information enhances the robustness of gait features and further improves recognition rate.