by S Pietzsch, M Wimmer, F Stulp and B Radig
Abstract:
In model-based fitting, the model parameters that best fit the image are determined by searching for the optimum of an objective function. Often, this function is designed manually, based on implicit and domain-dependent knowledge. We acquire more robust objective function by learning them from annotated images, in which many critical decisions are automated, and the remaining manual steps do not require domain knowledge. Still, the trade-off between generality and accuracy remains. General functions can be applied to a large range of objects, whereas specific functions describe a subset of objects more accurately. Gross et al. have demonstrated this principle by comparing generic to person-specific Active Appearance Models. As it is impossible to learn a person-specific objective function for the entire human population, we automatically partition the training images and then learn partition-specific functions. The number of groups influences the specificity of the learned functions. We automatically determine the optimal partitioning given the number of groups, by minimizing the expected fitting error. Our empirical evaluation demonstrates that the group-specific objective functions more accurately describe the images of the corresponding group. The results of this paper are especially relevant to face model tracking, as individual faces will not change throughout an image sequence.
Reference:
Face Model Fitting with Generic, Group-specific, and Person-specific Objective Functions (S Pietzsch, M Wimmer, F Stulp and B Radig), In 3rd International Conference on Computer Vision Theory and Applications (VISAPP), volume 2, 2008.
Bibtex Entry:
@inproceedings{pietzsch_face_2008,
author = {S Pietzsch and M Wimmer and F Stulp and B Radig},
title = {Face Model Fitting with Generic, Group-specific, and Person-specific
Objective Functions},
booktitle = {3rd International Conference on Computer Vision Theory and Applications
({VISAPP)}},
year = {2008},
volume = {2},
pages = {5--12},
address = {Madeira, Portugal},
month = {jan},
abstract = {In model-based fitting, the model parameters that best fit the image
are determined by searching for the optimum of an objective function.
Often, this function is designed manually, based on implicit and
domain-dependent knowledge. We acquire more robust objective function
by learning them from annotated images, in which many critical decisions
are automated, and the remaining manual steps do not require domain
knowledge. Still, the trade-off between generality and accuracy remains.
General functions can be applied to a large range of objects, whereas
specific functions describe a subset of objects more accurately.
Gross et al. have demonstrated this principle by comparing generic
to person-specific Active Appearance Models. As it is impossible
to learn a person-specific objective function for the entire human
population, we automatically partition the training images and then
learn partition-specific functions. The number of groups influences
the specificity of the learned functions. We automatically determine
the optimal partitioning given the number of groups, by minimizing
the expected fitting error. Our empirical evaluation demonstrates
that the group-specific objective functions more accurately describe
the images of the corresponding group. The results of this paper
are especially relevant to face model tracking, as individual faces
will not change throughout an image sequence.},
keywords = {facial expressions},
}