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2016FallSymposiumURCreativityEngagement has ended
Tuesday, December 6 • 1:40pm - 2:00pm
Computational Aesthetics

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In the developing field of computational aesthetics, researchers examine and define how machines judge beauty and creativity in a manner similar to humans. The universal indicators of beauty have been studied in numerous fields and through various methods. One psychological theory of attractiveness proposes that humans find average faces to be highly attractive. This theory has dated back to the late 1800’s during which Sir Francis Galton noted composites of faces to be more attractive than their individual components. We incorporate the theory of “average is attractive” in our ongoing attempts to numerically measure attractiveness of three-dimensional face models. Specifically, we investigate if we can determine attractiveness using Blanz and Vetter’s mathematical model of faces, which abstracts each face as a point in a multi-dimensional vector space with the origin as the average face. The axes of the facial point space are determined by a statistical analysis of a dataset containing approximately 200 faces. As implemented in the FaceGen modelling software package, each face is conceptualized as a point associated with 130 facial features involving both shape and texture. Using this representation and the hypothesis that attractive faces cluster together in vector space, we implemented an attractiveness measure by computing proximity to a parameter that we label as a “known” attractive face. In this presentation, we compare and discuss the results of this proximity algorithm with the human evaluations we receive from our aesthetics survey. Faculty and students evaluated 24 three-dimensional models which are split into four overlapping subgroups of 12 faces. We are presenting initial analysis of our results, including correlation between the judgement of individual raters.


Tuesday December 6, 2016 1:40pm - 2:00pm
125 Rhoades Robinson Hall