Compared with the conventional dictionary learning algorithm, the novel projection dictionary pair learning (DPL) algorithm introduces a projection dictionary in the dictionary learning process and utilizes the projection coding to replace the sparse coding in the dictionary for object samples. The computational cost of the pattern recognition algorithm can be effectively reduced. However, the original DPL algorithm is sensitive to occlusion and noise interference. To solve this problem, a discriminative low-rank projection dictionary pair learning (DLPL) algorithm is proposed. A low-rank constraint is added to the dictionary in the model and the least squares estimation method is used to constrain the classification error of the projection coding for the labeled samples. The unknown dictionary and the projection dictionary with closed solutions can be solved quickly by the alternative optimization method. Experimental results in different databases show that the DLPL algorithm can not only improve the performance of dictionary under occlusion and noise interference and raise the pattern recognition rate, but also effectively reduce the training time and the test time for the model.