A new approach to gait recognition based on fusion of the information of global silhouette and local joint angle is proposed The vector data scanned from horizon

vertical and diagonal of the outer contour of binarized silhouette of a walking person are chosen as the basic image feature Two independent global classifiers are established respectively by the decomposed feature based on the discrete wavelet transformation（DWT) and the nonlinear components of basic gait features extracted based on kernel principal component analysis（KPCA).The coax and knee joint of moving body are simply modeled.The acquired joint angle information is expanded in Fourier series form in view of the periodic character of gait activity. The genetic algorithm is applied to search for the expanding coefficients

and the local feature classifier is established by the normalized eigenvector about joint angle At last

the global and local features are fused based on different Bayesian combination rules on decision level to improve the performance of both identification and verification. This algorithm is applied to CMU database.Extensive experimental results demonstrate that the proposed algorithm performs nicer classification and verification capability