@conference {14830,
	title = {Localizing parts of faces using a consensus of exemplars},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {545 - 552},
	abstract = {We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset.},
	keywords = {Bayesian, faces;lighting;occlusion;pose;Bayes, function;exemplar, images;expression;face, localization;human, methods;face, objective, part, recognition;},
	doi = {10.1109/CVPR.2011.5995602},
	author = {Belhumeur,P. N. and Jacobs, David W. and Kriegman, D.J. and Kumar,N.}
}
