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
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
Original language | English |
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Pages (from-to) | 2628-2636 |
Number of pages | 9 |
Journal | Pattern Recognition |
Volume | 42 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2009 |