Semi-supervised Learning of Caricature Pattern from Manifold Regularization

Authors:
Junfa Liu;Yiqiang Chen;Jinjing Xie;Xingyu Gao;Wen Gao
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190 and Graduate School of Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190 and Graduate School of Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190 and Institute of Digital Media, Peking University, Beijing, China 100871
Venue:
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Year:
2009

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Abstract

Automatic caricature synthesis is to transform the input face to an exaggerated one. It is becoming an interesting research topic, but it remains an open issue to specify the caricature's pattern for the input face. This paper proposed a novel pattern prediction method based on MR (manifold regularization), which comprises three steps. Firstly, we learn the caricature pattern by manifold dimension reduction, and select some low dimensional caricature pattern as the labels for corresponsive true faces. Secondly, manifold regularization is performed to build a semi-supervised regression between true faces and the pattern labels. In the third step of offline phase, the input face is mapped to a pattern label by the learnt regressive model, and the pattern label is further transformed to caricature parameters by a locally linear reconstruction algorithm. This approach takes advantage of manifold structure lying in both true faces and caricatures. Experiments show that, low dimensional manifold represents the caricature pattern well and the semi-supervised regressive model from manifold regularization can predict the target caricature pattern successfully.