The existing Low-rank representation methods for face recognition fuse of local and global feature information of facial images inadequately. In order to solve the problem, a new face recognition method called Characterized Dictionary-based Low-Rank Representation (LRR-CD) was proposed. Firstly, every face image was represented as a set of characterized patches, then the low-rank reconstruction characteristic coefficients based on training samples as well as the corresponding intra-class characteristic variance were minimized. To obtain the efficient and high discriminative reconstruction coefficient matrix of face image patches, a new mathematical formula was presented. This formula could be used to completely preserve both global and local features of original hyper-dimensional face images, especially the local intra-class variance features, by the way of minimizing the low-rank constraint problem of corresponding patches in training samples and correlated intra-class variance dictionary. What's more, owing to the adequate mining of patch features, the proposed method obtained good robustness to the general noise such as facial occlusion and luminance variance. Several experiments were carried out on the face databases such as AR, CMU-PIE and Extended Yale B. The experimental results fully illustrate that the LRR-CD outperforms the compared algorithms of Sparse Representation Classification (SRC), Collaborative Representation Classification (CRC), LRR with Normalized CUT (LRR-NCUT) and LRR with Recursive Least Square (LRR-RLS), with the higher recognition rate of 2.58-17.24 percentage points. The proposed method can be effectively used for the global and local information fusion of facial features and obtains a good recognition rate.