In order to improve the recognition rate and speed of face recognition, this paper proposes a face recognition algorithm based on Gabor featutre and projective dictionary pair learning.Firstly, we extract the local feature of the image at multiple scales and orientations by using Gabor transform, in that Gabor featutre shows significant robustness to the variations in expression, illumination and angle.And we obtain the augmented Gabor feature reduced dimension by principle component analysis (PCA) to form a new training data instead of original training sample.Secondly, a synthesis dictionary with reconshructing capability and an analysis dictionary with the capability of quickly obtaining representation coefficients are learned jointly during the training phase.Finally,the face is identified by the reconstructed error.The experimental results on ORL and extended YaleB database show that our proposed algorithm can get a high recognition rate and improve recognition speed efficiently.