Abstract: Recently,gait recognition for individual identification has been attracting increasing attention of biometrics researchers．It is well known that Gait Energy Image(GEI)is an efficient representation for gait,and Local Binary Pattern(LBP)can extract the local information efficiently．So,this paper used and Local Binary Pattern(LBP)to extract the local feature of gait energy image(GEI),and then it was used to identify．First of all,in order to extract local information better,the gait energy image(GEI) was segmented and the LBP features in each block were extracted and then each sub-block was fused in the feature layer to gain the whole gait energy image(GEI)’s features,at the same time,in order to explore the gait energy image(GEI)information better,this paper expanded the LBP model．Because the obtained LBP feature dimension is high,this paper used the Discriminant Common Vector(DCV)which has good dimensionality reduction and recognition ability to reduce the LBP features’ dimension．Finally,for simplicity consideration,we used the nearest neighbor classifier to classification．Experimental results on CASIA databases show that our algorithm is effective and obtains high recognition rates．

Key words: Gait energy image,Local binary pattern,Discriminant common vector,Dimension reduction,Gait recognition