Sparse Representation-based Super-Resolution for Face Recognition At a Distance

Face recognition is a challenging task, especially when low-resolution images or image sequences are used. A decrease in image resolution results in a loss of facial high frequency components leading to a decrease in recognition rates. In this paper, we propose a new method for super-resolution by building a dictionary of high-frequency components in the facial data, which are added to a low-resolution input image to create a super-resolved image. Our method is different from existing methods as we estimate the high-frequency components, 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.