A Unified Tensor-based Active Appearance Face Model
This work addresses face analysis problems for computer vision researchers, offering incremental improvements in modeling and synthesis capabilities.
The authors tackled the challenge of appearance variations in face image analysis by proposing a Unified Tensor-based Active Appearance Model (UT-AAM) that jointly models geometry and texture, enabling handling of pose variations and incomplete data. Experimental results on Multi-PIE and 300-W datasets demonstrate its utility, such as improving facial landmark detection through synthetic sample generation.
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.