Mapping learning in eigenspace for harmonious caricature generation

This paper proposes a mapping learning approach for caricature auto-generation. Simulating the artist’s creativity based on the object’s facial feature, our approach targets discovering what are the principal components of the facial features, and what’s the difference between facial photograph and caricature measured by those components. In training phase, PCA approach is adopted to obtain the principal components. Then, machine learning of SVR (Support Vector Regression) is carried out to learn the mapping model in principal component space. With the mapping model, in application phase, users just need to input a frontal facial photograph for the caricature generation. The caricature is exaggerated based on the original face while reserving essential similar features. Experiments proved comparatively that our approach could generate more harmonious caricatures. Categories and Subject Descriptors

• Type: Conference
• Year: 2006
• Where: MM
• Authors: Junfa Liu, Yiqiang Chen, Wen Gao
• Keywords: Caricature, Facial Features, MM 2006, Principal Component