In GEI, because binarized silhouettes are averaged over full gait sequence, binarized silhouettes can only capture edge information at the boundary of the person. Based on the defects of GEI algorithm, a gradient histogram energy image algorithm for person identification was proposed. the gradient histogram energy image (GHEI) can capture edge information at the boundary of the person, but also captures edges within the person by means of gradient histograms. The process of gradient histogram energy image on the foreground of each frame (FEFGHEI) calculation can be detailed as follows: First, the foregrounds are segmented from each frame. And then, we calculate HOG on the foreground of each frame separately. Finally, the resulting gradient histograms are averaged over full gait cycles. On this basis, according to the idea of gait energy image (GEI) and histograms of oriented gradients (HOG), the four variations of energy images proposed . The proposed five experiments and gait energy image (GEI) were run on the widely used CASIA gait database and carry on the analysis comparison, The proposed methods show significant performance improvements over the current state of the art.