Additive Update Predictors in Active Appearance Models

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.

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Philip A. Tresadern, Patrick Sauer, Timothy F. Cootes
• Keywords: Additive Models, BMVC 2010, Computer Vision, Linear, Linear Additive Models