  Single-sample face recognition is one of the most challenging problems in
face recognition. We propose a novel algorithm to address this problem based on
a sparse representation based classification (SRC) framework. The new algorithm
is robust to image misalignment and pixel corruption, and is able to reduce
required gallery images to one sample per class. To compensate for the missing
illumination information traditionally provided by multiple gallery images, a
sparse illumination learning and transfer (SILT) technique is introduced. The
illumination in SILT is learned by fitting illumination examples of auxiliary
face images from one or more additional subjects with a sparsely-used
illumination dictionary. By enforcing a sparse representation of the query
image in the illumination dictionary, the SILT can effectively recover and
transfer the illumination and pose information from the alignment stage to the
recognition stage. Our extensive experiments have demonstrated that the new
algorithms significantly outperform the state of the art in the single-sample
regime and with less restrictions. In particular, the single-sample face
alignment accuracy is comparable to that of the well-known Deformable SRC
algorithm using multiple gallery images per class. Furthermore, the face
recognition accuracy exceeds those of the SRC and Extended SRC algorithms using
hand labeled alignment initialization.
