In order to improve the classification rate of gait recognition, a new gait recognition algorithm is proposed. Firstly, the gait images are preprocessed, and the outlines of gait images are extracted and normalized. Secondly, wavelet moments of the outlines are calculated to describe the static feature of the gait images. Thirdly, the leg double triangle model is built. The first triangle consists of the mid-point of the two hips, left knee point and right knee point, and the other one consists of the mid-point of the two hips, left ankle point and right ankle point. Then the parameters of two triangles are extracted to describe the dynamic features of the gait images. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.