A new gait recognition method based on information fusion of multiple kinds of features and views is proposed in this paper. Through the background subtraction and shadow elimination

human motion silhouettes are obtained and gait features are extracted using pseudo-Zernike moment

wavelet descriptor and Procrustes shape analysis. The gait recognition is accomplished through information fusion of multiple kinds of features and views on feature level and decision level. The method is evaluated on the CASIA gait database and received comparative high correct recognition rate. The experimental results show that our approach has efficient recognition performance.