The appearance-based gait recognition methods are easily affected by the carrying objects, clothing and other occlusion factors. In order to solve this problem, Dynamic Gait Image is proposed. Dynamic Gait Image divide gait image into dynamic part and static part, which is more conductive to extract dynamic information less affected by occlusion factors. This paper proposes Bi-Route gait recognition network, which can minimize the influence of occlusion factors by increasing the proportion of dynamic features and reducing the proportion of static features. The global silhouettes features and frame level silhouettes features of the gait sequences were extracted by 2D-convolutional neural network with the input of dynamic gait image. Then 3D-convolutional neural network extracts dynamic features from frame level silhouettes features. The accuracy of the proposed method evaluated on CASIA-B dataset is 92. 9%, 87. 2% and 65. 6% in NM, BG and CL conditions. The result shows that the proposed method can reduce the impact of occlusion factors.