Active Appearance Models (AAMs) are widely used to fit shape models to new images. Recently it has been demonstrated that non-linear regression methods and sequences of AAMs can significantly improve performance over the original linear formulation. In this paper we focus on the ability of a model trained on one dataset to generalise to other sets with different conditions. In particular we compare two non-linear, discriminative regression strategies for predicting shape updates, a boosting approach and variants of Random Forest regression. We investigate the use of these regression methods within a sequential model fitting framework, where each stage in the sequence consists of a shape model and a corresponding regression model. The performance of the framework is assessed by both testing on unseen data taken from within the training databases, as well as by investigating the more difficult task of generalising to unrelated datasets. We present results that show that (a) the generalisation performance of the Random Forest is superior to that of the linear or boosted regression procedure and that (b) using a simple feature selection procedure, the Random Forest can be made to be as efficient as the boosting procedure without significant reduction in accuracy.
['Patrick Sauer', 'Timothy F. Cootes', 'Christopher J. Taylor']
Accurate Regression Procedures for Active Appearance Models