  The "interpretation through synthesis" approach to analyze face images,
particularly Active Appearance Models (AAMs) method, has become one of the most
successful face modeling approaches over the last two decades. AAM models have
ability to represent face images through synthesis using a controllable
parameterized Principal Component Analysis (PCA) model. However, the accuracy
and robustness of the synthesized faces of AAM are highly depended on the
training sets and inherently on the generalizability of PCA subspaces. This
paper presents a novel Deep Appearance Models (DAMs) approach, an efficient
replacement for AAMs, to accurately capture both shape and texture of face
images under large variations. In this approach, three crucial components
represented in hierarchical layers are modeled using the Deep Boltzmann
Machines (DBM) to robustly capture the variations of facial shapes and
appearances. DAMs are therefore superior to AAMs in inferencing a
representation for new face images under various challenging conditions. The
proposed approach is evaluated in various applications to demonstrate its
robustness and capabilities, i.e. facial super-resolution reconstruction,
facial off-angle reconstruction or face frontalization, facial occlusion
removal and age estimation using challenging face databases, i.e. Labeled Face
Parts in the Wild (LFPW), Helen and FG-NET. Comparing to AAMs and other deep
learning based approaches, the proposed DAMs achieve competitive results in
those applications, thus this showed their advantages in handling occlusions,
facial representation, and reconstruction.
