@conference {14832,
	title = {A deformation and lighting insensitive metric for face recognition based on dense correspondences},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {2353 - 2360},
	abstract = {Face recognition is a challenging problem, complicated by variations in pose, expression, lighting, and the passage of time. Significant work has been done to solve each of these problems separately. We consider the problems of lighting and expression variation together, proposing a method that accounts for both variabilities within a single model. We present a novel deformation and lighting insensitive metric to compare images, and we present a novel framework to optimize over this metric to calculate dense correspondences between images. Typical correspondence cost patterns are learned between face image pairs and a Nai $\#$x0308;ve Bayes classifier is applied to improve recognition accuracy. Very promising results are presented on the AR Face Database, and we note that our method can be extended to a broad set of applications.},
	keywords = {$\#$x0308;ve, Bayes, classifier;pose, comparison;lighting, correspondences;expression, deformation;dense, estimation;, insensitive, methods;face, metric;nai, recognition;image, recognition;pose, variation;Bayes, variation;face},
	doi = {10.1109/CVPR.2011.5995431},
	author = {Jorstad,A. and Jacobs, David W. and Trouve,A.}
}
@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.}
}
