  We present a low-rank transformation approach to compensate for face
variations due to changes in visual domains, such as pose and illumination. The
key idea is to learn discriminative linear transformations for face images
using matrix rank as the optimization criteria. The learned linear
transformations restore a shared low-rank structure for faces from the same
subject, and, at the same time, force a high-rank structure for faces from
different subjects. In this way, among the transformed faces, we reduce
variations caused by domain changes within the classes, and increase
separations between the classes for better face recognition across domains.
Extensive experiments using public datasets are presented to demonstrate the
effectiveness of our approach for face recognition across domains. The
potential of the approach for feature extraction in generic object recognition
and coded aperture design are discussed as well.
