Referring to both situations when the color of clothes people dressed is similar to the background color and when lower limbs of human body have the shadow in front-view gait recognition are hard to recognize, an improved background subtraction by using the different threshold at above and lower part of human body divided by the hip is proposed, which is used for extracting the binary contour of the human body. For cycle detection, first of all, we use hip ideas to find the optimal point P, and then obtain the lowest points of the left foot and right foot. The cycle is calculated by the angle based on the distance difference of the two feet, while the angle as a dynamic feature is used to describe the gait. The next step is to extract the key frames and be normalized, and the unified Hu moment features are extracted, being integrated with gait cycle and body proportion as the static features. The method of the fusion of static and dynamic feature solves the low recognition rate which is caused by the single feature. Finally, the Support Vector Machine (SVM) is used for classification. The recognition experiments of this paper are all trained in the Chinese Academy of Sciences gait database (CASIA), and the recognition rate is greater than 97%. The results show that the proposed approach has an encouraging recognition performance.