Analysis of Neural Network Based Photo to Caricature Transformation Using MATLAB

Abstract

Creating computer generated caricature from given photograph will be a great source of entertainment to all people because caricatures arefunny, humorous and satirical. To generate caricature it requires artificial intelligence to capture the non-linear relationships between photograph and drawn caricature. It means that computer has to think itself what will be caricature for the given photograph. A formalization of this idea, Exaggerating the Difference from the Mean (EDFM) is widely accepted among caricaturists to be the driving factor behind caricature generation. As artificial intelligence is based on neuralnetwork, the key research is required to investigate neural network and design its implementation. 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 modeling of a personalized exaggeration process,
leading to fully automatic caricature generation with increased accuracy. In this paper the author provides experimental results and detailed analysis to prove that a Cascade Correlation Neural Network (CCNN) can be successfully used for transforming caricature generation from photos.