Image-based Aging Using Evolutionary Computing

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Hubball:2008:CGF,
  author =       "Daniel Hubball and Min Chen and Phil W. Grant",
  title =        "Image-based Aging Using Evolutionary Computing",
  journal =      "Computer Graphics Forum",
  year =         "2008",
  volume =       "27",
  number =       "2",
  pages =        "607--616",
  note =         "EUROGRAPHICS 2008 / G. Drettakis and R. Scopigno
                 (Guest Editors)",
  keywords =     "genetic algorithms, genetic programming, I.3.3
                 Computer Graphics, Picture/Image Generation; I.3.6
                 Computer Graphics, Methodology and Techniques; I.2.8
                 Artificial Intelligence, Problem Solving, Control
                 Methods and Search",
  ISSN =         "1467-8659",
  publisher =    "Blackwell Publishing Ltd",
  URL =          "http://dx.doi.org/10.1111/j.1467-8659.2008.01158.x",
  DOI =          "doi:10.1111/j.1467-8659.2008.01158.x",
  abstract =     "Ageing has considerable visual effects on the human
                 face and is difficult to simulate using a
                 universally-applicable global model. In this paper, we
                 focus on the hypothesis that the patterns of age
                 progression (and regression) are related to the face
                 concerned, as the latter implicitly captures the
                 characteristics of gender, ethnic origin, and age
                 group, as well as possibly the person-specific
                 development patterns of the individual. We use a
                 data-driven framework for automatic image-based facial
                 transformation in conjunction with a database of facial
                 images. We build a novel parametrised model for
                 encoding age-transformation in addition with the
                 traditional model for face description. We use
                 evolutionary computing to learn the relationship
                 between the two models. To support this work, we also
                 developed a new image warping algorithm based on
                 non-uniform radial basis functions (NURBFs).
                 Evolutionary computing was also used to handle the
                 large parameter space associated with NURBFs. In
                 comparison with several different methods, it
                 consistently provides the best results against the
                 ground truth.",
  notes =        "Also known as \cite{CGF:CGF1158}",
}

Genetic Programming entries for Daniel Hubball Min Chen Phil W Grant

Citations