Although dictionary learning has shown to be a powerful tool for image representation and has achieved satisfactory results in various image recognition tasks. Most traditional dictionary learning algorithms have been restricted to multi-resolution face recognition tasks mainly due to the poor discriminability of the dictionary. To solve this problem, we propose a novel multi-resolution dictionary learning algorithm with discriminative locality constraints (MDLDLC) in this paper. Based on the one-to-one mapping between each dictionary atom and the corresponding profile vector, we design two local constraints on profile vectors, referred to as intra-class and inter-class local constraints, by utilizing the local geometric structure of the dictionary atoms. Meanwhile, the two constraints are formulated into a unified regularization term and incorporated into the objective function of the dictionary learning model to optimize for encoding the discriminative locality of input data jointly. The proposed MDLDLC algorithm encourages high intra-class local consistency and inter-class local separation in the code space of multi-resolution images. Finally, extensive experiments conducted on different multi-resolution face image datasets demonstrate the effectiveness of the proposed MDLDLC algorithm. The results show that the proposed MDLDLC algorithm can learn the multi-resolution dictionaries with discriminative locality, preserving and achieving promising recognition performance compared with other state-of-the-art dictionary learning algorithms.