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.

The Active Appearance Model (AAM) provides an efficient method for localizing objects that vary in both shape and texture, and uses a linear regressor to predict updates to model parameters based on current image residuals. This study investigates using additive (or 'boosted') predictors, both linear and non-linear, as a substitute for the linear predictor in order to improve accuracy and efficiency. We demonstrate: (a) a method for training additive models that is several times faster than the standard approach without sacrificing accuracy; (b) that linear additive models can serve as an effective substitute for linear regression; (c) that linear models are as effective as non-linear models when close to the true solution. Based on these observations, we compare a 'hybrid' AAM to the standard AAM for both the XM2VTS and BioID datasets, including cross-dataset evaluations. © 2010. The copyright of this document resides with its authors.