Colored exaggerative caricature creation using inter- and intra-correlations of feature shapes and positions

Chien Chung Tseng, Jenn Jier James Lien

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper develops a system comprising a statistics-based exaggerative (SBE) module and a non-photorealistic rendering (NPR) module for the automatic creation of colored facial caricatures with exaggerated facial features and individual facial details such as beards and moles. Unlike previous research that focused on the inter-correlation (the difference between the facial features of input image and those of the mean face in the training database), the SBE module exaggerates the input image utilizing an iterative approach based on both inter- and intra-correlations of the facial features. The intra-correlation considered in this study makes the comparison with other features within the same input image, and has the effect of exaggerating the major facial features while simultaneously subduing the visual impact of non-major facial features. The NPR module consists of a black-and-white sketch creation process and a colored facial cartoon creation process. The results of the two processes are combined to generate a colored cartoon-like sketch, which is then warped into a colored exaggerative facial caricature based on the corresponding exaggerative shape and position created by the SBE module. The experimental results demonstrate that the proposed method can emphasize the major characteristics of a face better than previous methods that only considered feature inter-correlation.

Original languageEnglish
Pages (from-to)15-25
Number of pages11
JournalImage and Vision Computing
Volume30
Issue number1
DOIs
Publication statusPublished - 2012 Jan

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition

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