  Face alignment (or facial landmarking) is an important task in many
face-related applications, ranging from registration, tracking and animation to
higher-level classification problems such as face, expression or attribute
recognition. While several solutions have been presented in the literature for
this task so far, reliably locating salient facial features across a wide range
of posses still remains challenging. To address this issue, we propose in this
paper a novel method for automatic facial landmark localization in 3D face data
designed specifically to address appearance variability caused by significant
pose variations. Our method builds on recent cascaded-regression-based methods
to facial landmarking and uses a gating mechanism to incorporate multiple
linear cascaded regression models each trained for a limited range of poses
into a single powerful landmarking model capable of processing arbitrary posed
input data. We develop two distinct approaches around the proposed gating
mechanism: i) the first uses a gated multiple ridge descent (GRID) mechanism in
conjunction with established (hand-crafted) HOG features for face alignment and
achieves state-of-the-art landmarking performance across a wide range of facial
poses, ii) the second simultaneously learns multiple-descent directions as well
as binary features (SMUF) that are optimal for the alignment tasks and in
addition to competitive landmarking results also ensures extremely rapid
processing. We evaluate both approaches in rigorous experiments on several
popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D Face
datasets and image collections F and G from the University of Notre Dame. The
results of our evaluation show that both approaches are competitive in
comparison to the state-of-the-art, while exhibiting considerable robustness to
pose variations.
