  Recognition of low resolution face images is a challenging problem in many
practical face recognition systems. Methods have been proposed in the face
recognition literature for the problem which assume that the probe is low
resolution, but a high resolution gallery is available for recognition. These
attempts have been aimed at modifying the probe image such that the resultant
image provides better discrimination. We formulate the problem differently by
leveraging the information available in the high resolution gallery image and
propose a dictionary learning approach for classifying the low-resolution probe
image. An important feature of our algorithm is that it can handle resolution
change along with illumination variations. Furthermore, we also kernelize the
algorithm to handle non-linearity in data and present a joint dictionary
learning technique for robust recognition at low resolutions. The effectiveness
of the proposed method is demonstrated using standard datasets and a
challenging outdoor face dataset. It is shown that our method is efficient and
can perform significantly better than many competitive low resolution face
recognition algorithms.
