Technical University of Munich

@inproceedings{wimmer_learning_2007,
 author = {M Wimmer and S Pietzsch and F Stulp and B Radig},
 title = {Learning Robust Objective Functions with Application to Face Model
	Fitting},
 booktitle = {Proceedings of the 29th {DAGM} Symposium},
 year = {2007},
 volume = {1},
 pages = {486--496},
 address = {Heidelberg, Germany},
 month = {sep},
 abstract = {Model-based image interpretation extracts high-level information from
	images using a priori knowledge about the object of interest. The
	computational challenge is to determine the model parameters that
	best match a given image by searching for the global optimum of the
	involved objective function. Unfortunately, this function is usually
	designed manually, based on implicit and domain-dependent knowledge,
	which prevents the fitting task from yielding accurate results. In
	this paper, we demonstrate how to improve model fitting by learning
	objective functions from annotated training images. Our approach
	automates many critical decisions and the remaining manual steps
	hardly require domain-dependent knowledge. This yields more robust
	objective functions that are able to achieve the accurate model fit.
	Our evaluation uses a publicly available image database and compares
	the obtained results to a recent state-of-the-art approach.},
 keywords = {facial expressions},
}