We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and reasons over binary occlusion labels. Our occlusion
reasoning proceeds by a hypothesize-and-test search over occlusion labels.
Hypotheses are generated by Constrained Local Model based shape fitting over
randomly sampled subsets of landmark detector responses and are evaluated by
the quality of face alignment. To span the entire range of facial pose and
expression variations we adopt an ensemble of independent Robust Constrained
Local Models to search over a discretized representation of pose and
expression. We perform extensive evaluation on a large number of face images,
both occluded and unoccluded. We find that our face alignment system trained
entirely on facial images captured "in-the-lab" exhibits a high degree of
generalization to facial images captured "in-the-wild". Our results are
accurate and stable over a wide spectrum of occlusions, pose and expression
variations resulting in excellent performance on many real-world face datasets.

研究提出了一种基于鲁棒受限局部模型的集成，用于面部对齐，可以在存在显着遮挡和任何未知的姿势和表情的情况下实现。通过假设和测试搜索来评估 occlusion，结果表明在测试集中表现出一定的鲁棒性。