Evolutionary Morphing for Facial Aging Simulation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Hubball:2007:ICSC,
  author =       "D. Hubball and M. Chen and P. W. Grant and D. Cosker",
  title =        "Evolutionary Morphing for Facial Aging Simulation",
  booktitle =    "International Crime Science Conference (ICSC 2007)",
  year =         "2007",
  address =      "UCL, London",
  month =        "16 " # jul,
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence, problem solving, control methods and
                 search, computer graphics, picture image generation,
                 methodology and techniques, image processing and
                 computer vision:, reconstruction, image metamorphosis,
                 morphing, warping, nonuniform radial basis functions,
                 facial aging, face modelling, evolutionary computing,
                 data-driven modelling",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.205.6838",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6838",
  abstract =     "Aging has considerable effects on the appearance of
                 the human face and is difficult to simulate using a
                 universally-applicable global model. In this paper, we
                 present a data-driven framework for facial age
                 progression (and regression) automatically in
                 conjunction with a database of facial images. We build
                 parametrised local models for face modelling,
                 age-transformation and image warping based on a subset
                 of imagery data selected according to an input image
                 and associated metadata. In order to obtain a
                 person-specific mapping in the model space from an
                 encoded face description to an encoded
                 age-transformation, we employed genetic programming to
                 automatically evolve a solution by learning from
                 example transformations in the selected subset. In
                 order to capture various factors that determine the
                 influence of feature points, we developed a new image
                 warping algorithm based on non-uniform radial basis
                 functions (NURBFs). A genetic algorithm was used to
                 handle the large parameter space associated with
                 NURBFs. With evolutionary computing, our approach is
                 able to infer from the input and the database the most
                 appropriate models to be used for transforming the
                 input face. We compared our data-driven approach with
                 the traditional global model approach. The noticeable
                 improvement in terms of the resemblance between the
                 output images and the actual target images (which are
                 unknown to the process) demonstrated the effectiveness
                 and usability of this new approach.",
  notes =        "Daniel Hubball and Min Chen and Phil W. Grant and
                 Darren Cosker See also technical report CSR 6-2006
                 http://www.cs.swansea.ac.uk/reports/yr2006/CSR6-2006.pdf
                 http://www.ucl.ac.uk/scs/events/crime-science-conf/icsc-2007",
}

Genetic Programming entries for Daniel Hubball Min Chen Phil W Grant Darren Cosker

Citations