Recognition tasks in very low-resolution (VLR) images are more challenging than those in high-resolution (HR) due to lack of adequate discriminative information. Previous VLR and HR coupled learning scheme limits both the representation and discriminative ability of features. In this work, we propose a semi-coupled locality-constrained representation (SLR) approach to learn the discriminative representations and the mapping relationship between VLR and HR features simultaneously. Both VLR and HR local manifold geometries are coded during representation, while the learned mapping function improves the manifold consistency by transforming VLR features to HR ones. Finally, the resolutionrobust features are fed into a sparse representation based classifier (SRC) to predict the face labels. The proposed algorithm gives better performance than many state-of-the-art VLR recognition algorithms.