In terms of the scarcity of single feature information in 3D face recognition,a recognition algorithm based on the fusion of global and local information is proposed in this paper for improving the recognition accuracy.Firstly,we make a surface fitting on the preprocessed 3D cloudy points by using multilevel B-spline and acquire an accurate face surface fitting function.And then the control points of the function are mapped into the range image,and the central profile and the horizontal outline cross the nose top are extracted according to the surface function and physiological characteristics of the face.Secondly,2D principal component analysis(PCA) is applied on the range image for extracting global information,and the contours are matched using modified ICP algorithm as local information.Finally,the weighted sum method is used to achieve information fusion at decision stage.The recognition test is executed on CASIA 3D face database,and experimental results demonstrate that the method is obviously superior to the methods with single feature information and it is also robust to posture without increasing algorithm complexity.