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@TECHREPORT{wuLD08tr,
  author = {Wu, H. and Liu, X. and Doretto, G.},
  title = {Face alignment via boosted ranking models},
  institution = {GE Global research},
  year = {2008},
  number = {2008GRC239},
  address = {Niskayuna, NY, USA},
  month = apr,
  note = {Visualization and Computer Vision Laboratory},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Video Surveillance, Appearance Modeling, Shape and Appearance Modeling,
	Integral Image Computations, Face Tracking, Face Modeling},
  abstract = {Face alignment seeks to deform a face model to match it with the features
	of the image of a face by optimizing an appropriate cost function.
	We propose a new face model that is aligned by maximizing a score
	function, which we learn from training data, and that we impose to
	be concave. We show that this problem can be reduced to learning
	a classifier that is able to say whether or not by switching from
	one alignment to a new one, the model is approaching the correct
	fitting. This relates to the ranking problem where a number of instances
	need to be ordered. For training the model, we propose to extend
	GentleBoost [22] to ranklearning. Extensive experimentation shows
	the superiority of this approach to other learning paradigms, and
	demonstrates that this model exceeds the alignment performance of
	the state-of-the-art.},
  file = {wuLD08tr.pdf:doretto\\report\\wuLD08tr.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\report\wuLD08tr.pdf},
  timestamp = {2006.11.29}
}