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Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem. Our approach is to apply a reconstruction-based super-resolution algorithm. Such an algorithm, however, has two main problems: first, it requires relatively similar images with not too much noise and second is that its improvement factor is limited by a factor close to two. To deal with the first problem we introduce a three-step approach, which produces a face-log containing images of similar frontal faces of the highest possible quality. To deal with the second problem, limited improvement factor, we use a learning-based super-resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. This results in an improvement factor of four for the entire system. The proposed system has been tested on 122 low-resolution sequences from two different databases. The experimental results show that the proposed system can indeed produce a high-resolution and good quality frontal face image from low-resolution video sequences.