Recently

gait recognition for individual identification has been attracting increasing attention from 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

but the information lack of the orientation and scale characteristic

Gabor wavelet can extract the feature of different orientation and scales. First

using Gabor wavelet to extract the different orientation and scales-information of GEI

the magnitude spectral image is obtained. Second

LBP is used to extract the local information from magnitude spectral image

it can extract more local orientation and scale feature than the method of directly use LBP on GEI. At last

as the dimension of the LBP feature is usually very high

this paper employs a popular method called Discriminative Common Vectors (DCV) for further dimensionality reduction

which minimizes the within-class distance and maximizes the between-class distance as much as possible. Finally

for simplicity consideration

the nearest neighbor classifier to classification is used. Experimental results on CASIA databases show that our algorithm is effective and obtains high recognition rates. Further

a sample expand method is proposed for the small sample problem in gait recognition

the method increase the recognition rates.