To　improve　the　gait　recognition　rate,　a　gait　recognition　method　based　on　wavelet　packet　decomposition　(WPD)　and　two-directional　two-dimensional　principal　component　analysis　((2D)2PCA)　was　proposed.　In　the　method,　gait　energy　image　(GEI)　of　the　body　silhouette　was　firstly　adopted　to　solve　the　problem　of　huge　gait　data.　And　then,　WPD　and　(2D)2PCA　were　used　to　extract　features　to　solve　the　problem　existing　in　the　gait　recognition　method　based　on　wavelet　transform　at　present:　high　frequency　component　loss　or　high　dimension　problem.　The　Experiment　evaluation　was　conducted　on　NLPR　gait　database　and　compared　with　classical　methods.　Result　shows　that　the　proposed　method　has　a　higher　recognition　rate　and　is　more　robust　to　the　change　of　view.