@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.}
}
@article {14869,
	title = {Face Verification Across Age Progression Using Discriminative Methods},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {5},
	year = {2010},
	month = {2010/03//},
	pages = {82 - 91},
	abstract = {Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we study the problem by designing and evaluating discriminative approaches. These directly tackle verification tasks without explicit age modeling, which is a hard problem by itself. First, we find that the gradient orientation, after discarding magnitude information, provides a simple but effective representation for this problem. This representation is further improved when hierarchical information is used, which results in the use of the gradient orientation pyramid (GOP). When combined with a support vector machine GOP demonstrates excellent performance in all our experiments, in comparison with seven different approaches including two commercial systems. Our experiments are conducted on the FGnet dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. We found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. In addition, we find that image quality and eyewear present more of a challenge than facial hair.},
	keywords = {algorithms;support, databases;, dataset;age, FGnet, hair;gradient, information;image, information;recognition, machine;face, machines;visual, methods;eyewear;face, methods;support, orientation, orientation;gradient, progression;commercial, pyramid;hierarchical, quality;magnitude, recognition;gradient, systems;discriminative, vector, verification;facial},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2009.2038751},
	author = {Ling,Haibin and Soatto,S. and Ramanathan,N. and Jacobs, David W.}
}
@conference {12509,
	title = {How would you look as you age ?},
	booktitle = {Image Processing (ICIP), 2009 16th IEEE International Conference on},
	year = {2009},
	month = {2009/11//},
	pages = {53 - 56},
	abstract = {Facial appearances change with increase in age. While generic growth patterns that are characteristic of different age groups can be identified, facial growth is also observed to be influenced by individual-specific attributes such as one{\textquoteright}s gender, ethnicity, life-style etc. In this paper, we propose a facial growth model that comprises of transformation models for facial shape and texture. We collected empirical data pertaining to facial growth from a database of age-separated face images of adults and used the same in developing the aforementioned transformation models. The proposed model finds applications in predicting one{\textquoteright}s appearance across ages and in performing face verification across ages.},
	keywords = {age-separated, appearances;facial, database;face, Face, growth, image, model;face, model;facial, models;facial, recognition;image, SHAPE, TEXTURE, texture;, transformation, verification;facial},
	doi = {10.1109/ICIP.2009.5413998},
	author = {Ramanathan,N. and Chellapa, Rama}
}
@article {12603,
	title = {Face Verification Across Age Progression},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/11//},
	pages = {3349 - 3361},
	abstract = {Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5\%},
	keywords = {age, Aging, Bayesian, classification;, classification;face, classifier;age, difference, effects;preprocessing, Face, image, images;error, methods;Bayes, methods;error, progression;age, rate;face, recognition, recognition;image, separated, statistics;face, systems;face, verification;facial},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2006.881993},
	author = {Ramanathan,N. and Chellapa, Rama}
}
