Recognizing the target face from the surveillance video using the given singletarget image is still a challenging issue in practical applications. Therefore, a lowresolution singletarget face recognition algorithm with singlesample is proposed in this work. Two significant limitations are taken into consideration, the sample numbers of the target objects and nontarget objects are seriously unbalanced in the probe set, and the single object problem cannot utilize the mutually exclusive relationship between different categories. In this paper, firstly, to increase the robustness of the openface face recognition, the clustering algorithm is utilized to transform the single object recognition problem into a multi classification recognition problem. Furthermore, the iterative label propagation algorithm is applied to optimize the attribution probability of the probe sample continuously. During the iteration, the face recognition threshold of each object is estimated according to the confidence probability. Hence, the single sample is capable to train the classifier. Finally, experimental results on multiple face datasets show that the proposed algorithm can achieve good performances in both accuracy and recall rate.