Face attractiveness improvement using beauty prototypes and decision

Abstract

To improve the attractiveness of a face, intuitively, one can drive the face approaching some beautiful faces. There are two major problem to solve for implementing the intuitive solution. One problem is that how to define and discover suitable beauty prototypes. Another is that how to determine the balance between the original face and the beauty prototype to produce the desired face. In this paper, we proposed a quantitive method to solve these two problems. First, a set of beautiful face prototypes are identified as cluster centers of beautiful faces, which avoid involving specific personal facial characteristic. Second, a beauty decision function is learned as a classifier that can tell whether a face is beautiful or not. Then, the facial attractiveness improvement procedure finds the nearest beauty prototype for the original face, and then approaches the prototype from the original face until the beauty decision function tells the approaching face is beautiful. With this method, the face is beautified and the difference between the beautified face and the original face is minimized. The experimental results verify the validity of the proposed methods.