A novel gait recognition method based on periodic sequence width images is proposed in order to gain gait quickly and correctly. This method transforms the 2D silhouette contours sequences to width vector sequences according to the gait cycle. The vector sequences are turned into the periodic sequence width images

presented by grey values. These grey values can exactly depict the gait motion. The periodic sequence width images contain both the static and dynamic gait characteristics

which not only keep the shape structure information of each frame

but also represent the variant movement information of gait sequence excellently. Furthermore

the new method greatly reduces the image dimension by discrete cosine transforms and adopts the radial basis function neural networks to identify the gait. Experiments prove this method is simple and effective in theory and application.