To reduce the influence of factors such as backpack load, clothing and environment on gait recognition rate, a full-body gait model fusing visual and tactile features was proposed. The model first took the support foot as the starting point, established the kinetic relationship between the mass of each body part and the ground support force according to the motion transfer process, and introduced visual features through acceleration. Then the model was parameter separated to obtain feature matrices representing different gait motion features, the visual and tactile features were extracted using visual image sequences and plantar pressure images obtained from Kinect and walkway-type plantar pressure meter. A database containing the three gait motion states of normal, backpack loaded and overcoat wearing was established. Finally the multi-classification method in support vector machine was selected to complete the gait recognition, and the classifier parameters were optimized by the K-CV method in the recognition process. The experimental results showed that the model recognition rate was improved by increasing the number of feature recognition points by means of plantar pressure partitioning. The average recognition rate of the model under normal gait motion conditions was 97.31%, and the recognition performance of the model decreased less in the case of backpack and wearing a coat. Fusion of visual and tactile features to build a full-body model including upper limb swing could effectively improve the robustness of the model under complex gait motion conditions and increase the gait recognition accuracy.