  Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a novel method for joint face landmark localization and frontal face
reconstruction (pose correction) using a small set of frontal images only. By
observing that the frontal facial image is the one with the minimum rank from
all different poses we formulate an appropriate model which is able to jointly
recover the facial landmarks as well as the frontalized version of the face. To
this end, a suitable optimization problem, involving the minimization of the
nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is
assessed in frontal face reconstruction (pose correction), face landmark
localization, and pose-invariant face recognition and verification by
conducting experiments on $6$ facial images databases. The experimental results
demonstrate the effectiveness of the proposed method.
