Sparsity Preserving Projections (SPP) is a recently proposed sparse subspace learning method which aims to preserve the sparse reconstructive relationship of the data. However, SPP is unsupervised and unsuitable for classification tasks. To extract the discriminant feature, Discriminant Sparsity Preserving Embedding (DSPE) is proposed. DSPE introduces Fisher criterion into the objective of SPP and emphasizes the discriminant information. On the other hand, Schmidt orthogonalizaiton is used to obtain the orthogonal basis vectors of the face subspace, which further enhances recognition performance. Experiments results on ORL and FERET face database indicate that the proposed DSPE has better effect on feature extraction and classification recognition.