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.
|Title of host publication||British Machine Vision Conference, BMVC 2010 - Proceedings|Br. Mach. Vis. Conf., BMVC - Proc.|
|Publication status||Published - 2010|
|Event||2010 21st British Machine Vision Conference, BMVC 2010 - Aberystwyth|
Duration: 1 Jul 2010 → …
|Conference||2010 21st British Machine Vision Conference, BMVC 2010|
|Period||1/07/10 → …|