Recently, structure-preserved projections (SPP) were proposed as a local matching-based algorithm for face recognition. Compared with other methods, the main advantage of SPP is that it can preserve the configural structure of subpatterns in each face image. However, the SPP algorithm ignores the information among samples from different classes, which may weaken its recognition performances. Moreover, the relationships of nearby pixels in the subpattern are also neglected in SPP. In order to address these limitations, a new algorithm termed spatially smoothed discriminant structure-preserved projections (SS-DSPP) is proposed. SS-DSPP takes advantage of the class information to characterize the discrimination structure of subpatterns from different classes, and a new spatially smooth constraint is also derived to preserve the intrinsic two-dimensional structure of each subpattern. The feasibility and effectiveness of the proposed algorithm are evaluated on four standard face databases (Yale, extended YaleB, CMU PIE, and AR). Experimental results demonstrate that our SS-DSPP outperforms the original SPP and several state-of-the-art algorithms.