by M Wimmer, S Fujie, F Stulp, T Kobayashi and B Radig
Abstract:
Due to their use of information contained in texture, Active Appearance Models (AAM) generally outperform Active Shape Models (ASM) in terms of fitting accuracy. Although many extensions and improvements over the original AAM have been proposed, on of the main drawbacks of AAMs remains its dependence on good initial model parameters to achieve accurate fitting results. In this paper, we determine the initial model parameters for AAM fitting with ASM fitting, and use machine learning techniques to improve the scope and accuracy of ASM fitting. Combining the precision of AAM fitting with the large radius of convergence of learned ASM fitting improves the results by an order of magnitude, as our empirical evaluation on a database of publicly available benchmark images demonstrates.
Reference:
An ASM Fitting Method Based on Machine Learning that Provides a Robust Parameter Initialization for AAM Fitting (M Wimmer, S Fujie, F Stulp, T Kobayashi and B Radig), In Proc. of the International Conference on Automatic Face and Gesture Recognition (FGR08), 2008.
Bibtex Entry:
@inproceedings{wimmer_asm_2008,
author = {M Wimmer and S Fujie and F Stulp and T Kobayashi and B Radig},
title = {An {ASM} Fitting Method Based on Machine Learning that Provides a
Robust Parameter Initialization for {AAM} Fitting},
booktitle = {Proc. of the International Conference on Automatic Face and Gesture
Recognition ({FGR08)}},
year = {2008},
address = {Amsterdam, Netherlands},
month = {sep},
abstract = {Due to their use of information contained in texture, Active Appearance
Models ({AAM)} generally outperform Active Shape Models ({ASM)} in
terms of fitting accuracy. Although many extensions and improvements
over the original {AAM} have been proposed, on of the main drawbacks
of {AAMs} remains its dependence on good initial model parameters
to achieve accurate fitting results. In this paper, we determine
the initial model parameters for {AAM} fitting with {ASM} fitting,
and use machine learning techniques to improve the scope and accuracy
of {ASM} fitting. Combining the precision of {AAM} fitting with the
large radius of convergence of learned {ASM} fitting improves the
results by an order of magnitude, as our empirical evaluation on
a database of publicly available benchmark images demonstrates.},
}