Abstract: In the real environment, such as surveillance circumstances, there are a large number of low-resolution (LR) faces which are needed to be recognized. Compared with high-resolution (HR) face, LR has less discriminative details, so its recognition is more difficult. In order to improve the LR face recognition accuracy, the construction of LR face recognition system use not only the LR faces but also the HR faces corresponding to the LR faces in recent research. But there are two deficiencies in them: 1) HR faces and LR faces are required to be all paired; 2) the construction of face recognition system does not utilize any class information. Actually, it is the fact that HR faces and LR faces are always partially paired (semi-paried) and their class labels are partially known (semi-supervised). As a result, a semi-paired and semi-supervised algorithm for LR face recognition is developed to overcome the deficiencies of the relevant research. For the sake of utilizing the semi-paired and semi-supervised data more effectiviely, the implementation of the algorithm is divided into two stages. One stage is semi-paired learning and the other stage is semi-supervised learning. Promising experiments results on the Yale and AR face databases show the feasibility and effectiveness of the proposed method.