@conference {13068,
	title = {Face verification using large feature sets and one shot similarity},
	booktitle = {Biometrics (IJCB), 2011 International Joint Conference on},
	year = {2011},
	month = {2011/10//},
	pages = {1 - 8},
	abstract = {We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12\% classification accuracy) while maintaining simplicity and good generalization ability.},
	keywords = {analysis;set, approximations;regression, descriptor;labeled, Face, feature, in, information;face, information;texture, least, LFW;PLS;PLS, recognition;least, regression;color, sets;one, shot, similarity;partial, squares, squares;shape, the, theory;, verification;facial, wild;large},
	doi = {10.1109/IJCB.2011.6117498},
	author = {Guo,Huimin and Robson Schwartz,W. and Davis, Larry S.}
}
