The collection of large-scale three-dimensional (3-D) face models has led to significant progress in the field of 3-D face alignment “in-the-wild,” with several methods being proposed toward establishing sparse or dense 3-D correspondences between a given 2-D facial image and a 3-D face model. Utilizing 3-D face alignment improves 2-D face alignment in many ways, such as alleviating issues with artifacts and warping effects in texture images. However, the utilization of 3-D face models introduces a new set of challenges for researchers. 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, subjects wearing eye-glasses, and so on). To this end, in this paper we propose the Multi-Attribute Robust Component Analysis (MA-RCA), a novel technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, elegantly incorporates knowledge from various available attributes, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">identity</i> . We evaluate the proposed method on problems such as UV denoising, UV completion, facial expression synthesis, and age progression, where MA-RCA outperforms compared techniques.