Multidimensional Scaling for Matching Low-Resolution Facial Images

Face recognition performance degrades considerably when the input images are of poor resolution as is often the case for images taken by surveillance cameras or from a large distance. In this paper, the authors propose a novel approach for the recognition of low resolution images using multidimensional scaling. From a resolution point of view, the scenario yielding the best performance is when both the probe and gallery images are of high enough resolution to discriminate across different subjects. The proposed method embeds the low resolution images in a Euclidean space such that the distances between them in the transformed space approximates the best distances had both the images been of high resolution.