This　paper　proposes　an　incomplete　GEI　gait　recognition　method　based　on　Random　Forests.　There　are　numerous　methods　exist　for　git　recognition,but　they　all　lead　to　high　dimensional　feature　spaces.　To　address　the　problem　of　high　dimensional　feature　space,　we　propose　the　use　of　the　Random　Forest　algorithm　to　rank　features＇　importance.　In　order　to　efficiently　search　throughout　subspaces,　we　apply　a　backward　feature　elimination　search　strategy.This　demonstrate　static　areas　of　a　GEI　also　contain　useful　information.Then,　we　project　the　selected　feature　to　a　low-dimensional　feature　subspace　via　the　newly　proposed　two-dimensional　locality　preserving　projections　(2DLPP)　method.Asa　sequence,we　further　improve　the　discriminative　power　of　the　extracted　features.　Experimental　results　on　the　CASIA　gait　database　demonstrate　the　effectiveness　of　the　proposed　method.　©　(2014)　Trans　Tech　Publications,　Switzerland.