Face recognition is a challenging task, especially when low-resolution images or image sequences are used. A decrease in image resolution typically results in loss of facial component details leading to a decrease in recognition rates. In this paper, we propose a new method for super-resolution by first learning the high-frequency components in the facial data that can be added to a low-resolution input image to create a super-resolved image. Our method is different from conventional methods as we estimate the high-frequency components, that are not used in other methods, to reconstruct a higher-resolution image, rather than studying the direct relationship between the high-and low-resolution images. Quantitative and qualitative results are reported for both synthetic and surveillance facial image databases.