  This paper presents a novel Robust Deep Appearance Models to learn the
non-linear correlation between shape and texture of face images. In this
approach, two crucial components of face images, i.e. shape and texture, are
represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines
(RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann
Machines, can separate corrupted/occluded pixels in the texture modeling to
achieve better reconstruction results. The two models are connected by
Restricted Boltzmann Machines at the top layer to jointly learn and capture the
variations of both facial shapes and appearances. This paper also introduces
new fitting algorithms with occlusion awareness through the mask obtained from
the RDBM reconstruction. The proposed approach is evaluated in various
applications by using challenging face datasets, i.e. Labeled Face Parts in the
Wild (LFPW), Helen, EURECOM and AR databases, to demonstrate its robustness and
capabilities.
