Authors:
Sylvia Pietzsch
1
;
Matthias Wimmer
2
;
Freek Stulp
1
and
Bernd Radig
3
Affiliations:
1
Technische Universität München, Germany
;
2
Perceptual Computing Lab, Faculty of Science and Engineering, Waseda University, Japan
;
3
Group of Cognitive Neuroinformatics, University of Bremen, Germany
Keyword(s):
Model fitting, person-specific, group-specific objective function.
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., 2005) 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.
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