@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}
}
@conference {12550,
	title = {Modeling shape and textural variations in aging faces},
	booktitle = {Automatic Face Gesture Recognition, 2008. FG {\textquoteright}08. 8th IEEE International Conference on},
	year = {2008},
	month = {2008/09//},
	pages = {1 - 8},
	abstract = {We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging.},
	keywords = {Aging, analysis;, faces;image, function;face, gradient;modeling, model;textural, recognition;image, shape;shape, texture;statistical, transformation, variations;texture},
	doi = {10.1109/AFGR.2008.4813337},
	author = {Ramanathan,N. and Chellapa, Rama}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
