Gait recognition for individual identification has received more and more attention from biometrics researchers. Gait Energy Image is an efficient represent method. Gabor wavelet was used to get magnitude feature of active region of gait energy image, but the images after the Gabor transform generate high dimension feature, which must be processed through effective feature fusion and selection. In order to overcome the shortcoming of high dimension feature of the traditional Gabor feature, a gait recognition method based on integrated Gabor feature is proposed in this paper. Firstly, by means of two integration methods are mean fusion and differential binary encoding, the active region Gabor feature images are integrated in a multiscale and multiangle way and 26 integrated Gabor feature images are obtained, and the 4 images from 26 integrated Gabor feature images are selected as the final feature vector. Finally, the feature vector is input KNN classifier to identify. Experimental results indicate that a gait recognition method based on integrated Gabor feature can separate and express gait features effectively, and reduce dimension and present expression data compactly, meanwhile the expressions are classified correctly.