Face recognition, being a widely utilized and profoundly challenging task within the field of computer vision, has witnessed the emergence of numerous practical methodologies in recent years. Among these, dictionary learning-based methods have garnered extensive attention and have found widespread application. Nevertheless, it is worth noting that the majority of dictionary learning methods suffer from certain limitations. On the one hand, their primary focus lies solely on the resolution of the original images, thereby rendering them susceptible to the impact of resolution changes encountered in real-world scenarios. On the other hand, their ability to acquire a robust dictionary is hindered by the inadequacy of available training samples. We propose a multi-resolution dictionary learning method based on sample extension and label embedding. We generate virtual samples for the original training samples and convert all samples into different resolutions. In addition, noise constraints are applied to the virtual samples to enhance robustness. Then, we construct the label embedding term using the label information of the atoms and embed it on a multi-resolution dictionary learning model for training. The recognition rates of the method on the ORL, extended Yale B, CMU PIE, and GT datasets are 93.72%, 92.57%, 97.79%, and 73.91%, respectively.