@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 {12586,
	title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {888 - 892},
	abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements},
	keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893346},
	author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama}
}
