  Despite much interest in face alignment in recent years, the large majority
of work has focused on near-frontal faces. Algorithms typically break down on
profile faces, or are too slow for real-time applications. In this work we
propose an efficient approach to face alignment that can handle 180 degrees of
head rotation in a unified way (e.g., without resorting to view-based models)
using 2D training data. The foundation of our approach is cascaded shape
regression (CSR), which has emerged recently as the leading strategy. We
propose a generalization of conventional CSRs that we call branching cascaded
regression (BCR). Conventional CSRs are single-track; that is, they progress
from one cascade level to the next in a straight line, with each regressor
attempting to fit the entire dataset. We instead split the regression problem
into two or more simpler ones after each cascade level. Intuitively, each
regressor can then operate on a simpler objective function (i.e., with fewer
conflicting gradient directions). Within the BCR framework, we model and infer
pose-related landmark visibility and face shape simultaneously using Structured
Point Distribution Models (SPDMs). We propose to learn task-specific feature
mapping functions that are adaptive to landmark visibility, and that use SPDM
parameters as regression targets instead of 2D landmark coordinates.
Additionally, we introduce a new in-the-wild dataset of profile faces to
validate our approach.
