At present, in the field of person re-identification, the commonly used supervised learning algorithms require a large size of labelled sample, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues in this paper, we make use of a small size of labelled sample to add discriminant information in the basic dictionary learning. Moreover, the sparse coefficients of dictionary learning are decomposed into a projection problem of the original features, and the projection matrix is trained by labelled samples, which is transformed into a metric learning problem. It thus integrates the advantages of the two methods through combining dictionary learning and metric learning. After the data is trained, a new projection matrix is used to project the unlabeled features into a new feature subspace and the labels of the samples are reconstructed. The semi-supervised learning problem is then transformed to a supervised learning problem with a Laplace term. Experiments on different public pedestrian datasets, such as VIPeR, PRID, iLIDS and CUHK01, show that the recognition accuracy of our method is better than some other existing person reidentification methods.