Evolutionary Morphing for Facial Aging Simulation
Inhaltsverzeichnis
Reference
D. Hubball and M. Chen and P. W. Grant and D. Cosker: Evolutionary Morphing for Facial Aging Simulation. International Crime Science Conference (ICSC 2007), 16 July 2007.
DOI
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 parameterized local mod- els for face modeling, 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 map- ping 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 transfor- mations 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 func- tions (NURBFs). A genetic algorithm was used to handle the large parameter space associated with NURBFs. With evolutionary com- puting, 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 effec- tiveness and usability of this new approach.
Extended Abstract
Bibtex
Used References
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