Technical University of Munich

@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},
}