  Constrained Local Models (CLMs) are a well-established family of methods for
facial landmark detection. However, they have recently fallen out of favor to
cascaded regression-based approaches. This is in part due to the inability of
existing CLM local detectors to model the very complex individual landmark
appearance that is affected by expression, illumination, facial hair, makeup,
and accessories. In our work, we present a novel local detector --
Convolutional Experts Network (CEN) -- that brings together the advantages of
neural architectures and mixtures of experts in an end-to-end framework. We
further propose a Convolutional Experts Constrained Local Model (CE-CLM)
algorithm that uses CEN as local detectors. We demonstrate that our proposed
CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial
landmark detection by a large margin on four publicly-available datasets. Our
approach is especially accurate and robust on challenging profile images.
