  Recently, due to the collection of large scale 3D face models, as well as the
advent of deep learning, a significant progress has been made in the field of
3D face alignment "in-the-wild". That is, many methods have been proposed that
establish sparse or dense 3D correspondences between a 2D facial image and a 3D
face model. The utilization of 3D face alignment introduces new challenges and
research directions, especially on the analysis of facial texture images. In
particular, texture does not suffer any more from warping effects (that
occurred when 2D face alignment methods were used). Nevertheless, since facial
images are commonly captured in arbitrary recording conditions, a considerable
amount of missing information and gross outliers is observed (e.g., due to
self-occlusion, or subjects wearing eye-glasses). Given that many annotated
databases have been developed for face analysis tasks, it is evident that
component analysis techniques need to be developed in order to alleviate issues
arising from the aforementioned challenges. In this paper, we propose a novel
component analysis technique that is suitable for facial UV maps containing a
considerable amount of missing information and outliers, while additionally,
incorporates knowledge from various attributes (such as age and identity). We
evaluate the proposed Multi-Attribute Robust Component Analysis (MA-RCA) on
problems such as UV completion and age progression, where the proposed method
outperforms compared techniques. Finally, we demonstrate that MA-RCA method is
powerful enough to provide weak annotations for training deep learning systems
for various applications, such as illumination transfer.
