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Data driven enhancement of facial

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    When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be dif? In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. The key component in our approach is an automatic facial attrac- tiveness engine trained on datasets of faces with accompanying fa- cial attractiveness ratings collected from groups of human raters. Given a new face, we extract a set of distances between a variety of facial feature locations, which de? We then search the face space for a nearby point with a higher predicted attractiveness rating.
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    [PDF] Data-driven enhancement of facial attractiveness | Semantic Scholar

    Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Cohen-Or and G.
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    Data-Driven Enhancement of Facial Attractiveness

    Abstract : When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal or the attractiveness of human faces in frontal photographs portraits , while maintaining close similarity with the original.
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