@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 {14860,
	title = {On the equivalence of common approaches to lighting insensitive recognition},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {1721 -1726 Vol. 2 - 1721 -1726 Vol. 2},
	abstract = {Lighting variation is commonly handled by methods invariant to additive and multiplicative changes in image intensity. It has been demonstrated that comparing images using the direction of the gradient can produce broader insensitivity to changes in lighting conditions, even for 3D scenes. We analyze two common approaches to image comparison that are invariant, normalized correlation using small correlation windows, and comparison based on a large set of oriented difference of Gaussian filters. We show analytically that these methods calculate a monotonic (cosine) function of the gradient direction difference and hence are equivalent to the direction of gradient method. Our analysis is supported with experiments on both synthetic and real scenes},
	keywords = {3D, conditions;lighting, cosine, difference;image, direction, filters;gradient, function;image, insensitive, intensity;lighting, recognition;image, recognition;lighting, scenes;Gaussian, segmentation;, variation;monotonic},
	doi = {10.1109/ICCV.2005.179},
	author = {Osadchy,M. and Jacobs, David W. and Lindenbaum,M.}
}
