View　variation　between　register　samples　and　unknown　samples　is　the　main　factor　affecting　gait　recognition　accuracy.　The　subspace　methods　can　effectively　avoid　the　influence　by　projecting　samples　into　a　common　subspace.　However,　most　of　them　project　samples　linearly　via　projection　matrices,　which　cannot　maintain　the　original　nonlinear　structure　of　multi-view　gait　data.　To　tackle　this　problem,　multi-nonlinear　multi-view　locality　preserving　projections　is　proposed　in　this　paper.　The　nonlinear　projection　is　realized　by　the　nonlinear　functions　family,　and　then　samples　are　projected　into　common　subspace　based　on　the　principle　of　locality　preserving　projections.　Finally,　the　unknown　samples　are　identified　by　nearest　neighbor　classification　in　the　common　subspace.　Experiments　on　the　CASIA(B)　gait　database　show　that　the　proposed　method　is　superior　to　other　projection　methods　under　multiple　combinations　of　views.　©　2019,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.