  Appearance variations result in many difficulties in face image analysis. To
deal with this challenge, we present a Unified Tensor-based Active Appearance
Model (UT-AAM) for jointly modelling the geometry and texture information of 2D
faces. For each type of face information, namely shape and texture, we
construct a unified tensor model capturing all relevant appearance variations.
This contrasts with the variation-specific models of the classical tensor AAM.
To achieve the unification across pose variations, a strategy for dealing with
self-occluded faces is proposed to obtain consistent shape and texture
representations of pose-varied faces. In addition, our UT-AAM is capable of
constructing the model from an incomplete training dataset, using tensor
completion methods. Last, we use an effective cascaded-regression-based method
for UT-AAM fitting. With these advancements, the utility of UT-AAM in practice
is considerably enhanced. As an example, we demonstrate the improvements in
training facial landmark detectors through the use of UT-AAM to synthesise a
large number of virtual samples. Experimental results obtained using the
Multi-PIE and 300-W face datasets demonstrate the merits of the proposed
approach.
