Person re-identification is one of the important issues addressed in computer vision. Existing recognition system concerns the matching of pedestrians across over-lapping cameras. When assuming pedestrian images as one representation of the camera view, person re-identification can be considered as a multi-view learning problem directly. On the basis of this assumption, a pedestrian recognition algorithm is proposed via canonical correlation analysis. Since the canonical correlation analysis is a linear dimensionality reduction algorithm, it is hard to extract semantic information for person re-identification (such as low resolution of images, changing illumination and other factors). A sparsity learning based person re-identification algorithm (SLR) is proposed. First, SLR obtained the semantic information of each camera view by the sparse learning, and then mapped the high-level features t into a public hidden space in order to make characteristic distance between different views can be compared. SLR aims to obtain more discriminable public hidden space. Finally, improve the matching rate of person re-identification across disjoint camera views. Comparing the proposed method and other common methods on the VIPeR dataset and CUHK campus dataset, experimental results show that the proposed method has higher recognition efficiency.