Use of Neural Networks in Automatic Caricature Generation: An Approach Based on Drawing Style Capture

  • Rupesh N. Shet
  • Ka H. Lai
  • Eran A. Edirisinghe
  • Paul W. H. Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)


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.


Neural Network Facial Image Hide Neuron Training Case Trained Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Brennan, S.E.: Caricature Generator: The Dynamic Exaggeration of Faces by Computer. Leonardo 18(3), 70–178 (1985)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Benson, P.J., Perrett, D.I.: Synthesising Continuous-tone Caricatures. Image & Vision Computing 9, 123–129 (1991)CrossRefGoogle Scholar
  3. 3.
    Rhodes, G., Tremewan, T.: Averageness, Exaggeration and Facial Attractiveness. Psychological Science 7, 105–110 (1996)CrossRefGoogle Scholar
  4. 4.
    Langlois, J.H., Roggman, L.A., Mussleman, L.: What Is Average and What Is Not Average About Attractive Faces. Psychological Science 5, 214–220 (1994)CrossRefGoogle Scholar
  5. 5.
    Redman, L.: How to Draw Caricatures. McGraw-Hill Publishers, New York (1984)Google Scholar
  6. 6.
    Neural Network, (Access date October 13, 2004)
  7. 7.
    The Maths Works Inc, User’s Guide version 4, Neural Network Toolbox, MATLAB Google Scholar
  8. 8.
    Fahlman, S.E.: The Cascade-Correlation Learning Architecture, Technical Report CMUCS- 90-100, School of Computer Science, Carnegie Mellon University (1990) Google Scholar
  9. 9.
    Carling, A.: Introducing Neural Networks. Sigma Press, Wilmslow (1992)zbMATHGoogle Scholar
  10. 10.
    Fausett, L.: Fundamentals of Neural Networks. Prentice-Hall, New York (1994)zbMATHGoogle Scholar
  11. 11. (Access date November 11, 2004)
  12. 12.
    Fujimiya, M.: “Morpher” (Access date November 11, 2004)

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rupesh N. Shet
    • 1
  • Ka H. Lai
    • 1
  • Eran A. Edirisinghe
    • 1
  • Paul W. H. Chung
    • 1
  1. 1.Department of Computer ScienceLoughborough UniversityUK

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