Abstract: The task of person re-identification is to associate individuals who have been observed over disjoint camera views.Due to its value in applications of video surveillance, person re-identification has drawn great attention from computer vision and machine learning communities.To address this problem, current literature mainly focuses on extracting discriminative features or learning distance metrics from pedestrian images.However, the representation power of learned features or metrics might be limited, because a person’s appearance usually undergoes large variations in different camera views, and many passers-by may take similar visual appearances in public spaces.In order to overcome these challenges and improve the person re-identification accuracies, we propose an effective re-identification method called cross-view discriminative dictionary learning with metric embedding.Different from traditional dictionary learning or metric learning approaches, the cross-view dictionary and distance metric are jointly learned in our model, thus their strengths can be combined. The proposed model not only captures the intrinsic relationships of representation coefficients, but also explores the distance constraints in different camera views. As a result, the re-identification can be performed with much more powerful representations in a discriminative subspace.To address the bias brought by unbalanced training samples in the metric learning phase, an automatic weighting strategy of training pairs is introduced.We devise an efficient optimization algorithm to solve the proposed model, in which the representation coefficients, dictionary, and metric are optimized alternately. Experimental results on three public benchmark datasets including VIPeR, GRID, and 3DPeS, show that the proposed method achieves remarkable performance compared with existing approaches as well as published results.