Data-Driven Enhancement of Facial Attractiveness

A new beautification software that transforms a face to make it more attractive while maintaining its "individuality" and recognizability. Link to paper and supporting information. Below is a technical video explaining the method. On the left are examples of original and beautified test images. The NY Times has an article on the subject.




ACM SIGGRAPH 2008

Data-Driven Enhancement of Facial Attractiveness

Tommer Leyvand, Daniel Cohen-Or, Gideon Dror and Dani Lischinski

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.

The key component in our approach is an automatic facial attractiveness engine trained on datasets of faces with accompanying facial 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 define a point in a high-dimensional “face space”. We then search the face space for a nearby point with a higher predicted attractiveness rating. Once such a point is found, the corresponding facial distances are embedded in the plane and serve as a target to define a 2D warp field which maps the original facial features to their adjusted locations. The effectiveness of our technique was experimentally validated by independent rating experiments, which indicate that it is indeed capable of increasing the facial attractiveness of most portraits that we have experimented with.

Link (pdf)