To be published in Pattern Recognition. Accepted 20 April 2009,
available online 4 May 2009
Abstract
The active appearance model (AAM) is a powerful method for modeling and
segmenting deformable visual objects. The utility of the AAM stems from
two fronts: its compact representation as a linear object class and its
rapid fitting procedure, which utilizes fixed linear updates. Although the
original fitting procedure works well for objects with restricted
variability when initialization is close to the optimum, its efficacy
deteriorates in more general settings, with regards to both accuracy and
capture range. In this paper, we propose a novel fitting procedure where
training is coupled with, and directly addresses, AAM fitting in its
deployment. This is achieved by simulating the conditions of real fitting
problems and learning the best set of fixed linear mappings, such that
performance over these simulations is optimized. The power of the approach
does not stem from an update model with larger capacity, but from
addressing the whole fitting procedure simultaneously. To motivate the
approach, it is compared with a number of existing AAM fitting procedures
on two publicly available face databases. It is shown that this method
exhibits convergence rates, capture range and convergence accuracy that
are significantly better than other linear methods and comparable to a
nonlinear method, whilst affording superior computational efficiency.