Use of neural networks in automatic caricature generation: an approach based on drawing style capture

Authors:
Rupesh N. Shet;Ka H. Lai;Eran A. Edirisinghe;Paul W. H. Chung
Affiliations:
Department of Computer Science, Loughborough University, UK;Department of Computer Science, Loughborough University, UK;Department of Computer Science, Loughborough University, UK;Department of Computer Science, Loughborough University, UK
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

Caricature is emphasizing the distinctive features of a particular face. Exaggerating the Difference from the Mean (EDFM) is widely accepted among caricaturists to be the driving factor behind caricature generation. However the caricatures created by different artists have different drawing style. No attempt has been taken in the past to identify these distinct drawing styles. Yet the proper identification of the drawing style of an artist will allow the accurate modelling of a personalised exaggeration process, leading to fully automatic caricature generation with increased accuracy. In this paper we provide experimental results and detailed analysis to prove that a Cascade Correlation Neural Network (CCNN) can be used for capturing the drawing style of an artist and thereby used in realistic automatic caricature generation. This work is the first attempt to use neural networks in this application area and have the potential to revolutionize existing automatic caricature generation technologies.