  This paper studies the problem of aligning a set of face images of the same
individual into a normalized image while removing the outliers like partial
occlusion, extreme facial expression as well as significant illumination
variation. Our model seeks an optimal image domain transformation such that the
matrix of misaligned images can be decomposed as the sum of a sparse matrix of
noise and a rank-one matrix of aligned images. The image transformation is
learned in an unsupervised manner, which means that ground-truth aligned images
are unnecessary for our model. Specifically, we make use of the remarkable
non-linear transforming ability of generative adversarial network(GAN) and
guide it with low-rank generation as well as sparse noise constraint to achieve
the face alignment. We verify the efficacy of the proposed model with extensive
experiments on real-world face databases, demonstrating higher accuracy and
efficiency than existing methods.
