Evolutionary Morphing for Facial Aging Simulation

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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