Applying Various Artists' Style of Exaggeration to a Facial Caricature Drawing System with an Interactive Genetic Algorithm

Abstract

We propose a prototype system that can be used by anyone to synthesize a caricature with not only the features of the target face but also the exaggeration he desires. First, we let 13 experts at drawing caricatures use our tool to synthesize line drawings corresponding to 5 targets' faces. Then, we learned their exaggeration rules from the synthesized line drawings by using neural networks. Our system traces the target face with a neutral facial expression and transforms it automatically into caricatures with various styles of exaggeration by using the following three types of exaggeration rules. The first type is the proposed exaggeration rule learned from the experts. The second is the traditional rule of simple extension of the geometric distance from an average face. The third is random deformation. Starting with one of these caricature types, a user can synthesize a caricature using an interactive genetic algorithm (IGA). We let 13 users synthesize their desired caricatures starting with caricatures generated only by exaggeration learned from experts and those generated only by extension of the geometric distance from an average face. Results of this experiment show that each type of exaggeration has advantages. These results also demonstrate the effectiveness of our system.