Abstract: Most recognition algorithms are for 2D data with strict restrictions,these algorithms are easily affected by pose,illumination and other factors.The reason is due to the shortage of information.To resolve this problem,we present a face recognition algorithm based on fusing 2D and 3D information.Being different from other algorithms,the input of our algorithm is one single 2D gray-scale image and 3D information is provided by reconstructed the 3D model.For the 2D image,we choose LBP feature as face representation feature.For the 3D model,we define 54 feature points,calculate geodesic distances between the nose tip and other feature points as 3D feature.A weighted sum of the 2D and 3D scores is used to deliver the fusion process and the weights are determined based on Fisher Linear Discriminant Analysis.Finally,the presented algorithm is tested on CAS-PEAL-R1 face database with illumination.