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Hybrid learning-based model for exaggeration style of facial caricature

Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeratio...

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Main Author: Sadimon, Suriati
Format: Thesis
Language:English
Published: 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/78996/1/SuriatiSadimonPFC2017.pdf
http://eprints.utm.my/id/eprint/78996/
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spelling my.utm.789962018-09-19T05:22:42Z http://eprints.utm.my/id/eprint/78996/ Hybrid learning-based model for exaggeration style of facial caricature Sadimon, Suriati QA75 Electronic computers. Computer science Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeration style of an artist is difficult to be coded in algorithmic method. Fortunately, artificial neural network, which possesses self-learning and generalization ability, has shown great promise in addressing the problem of capturing and learning an artist’s style to predict a facial caricature. However, one of the main issues faced by this study is inconsistent artist style due to human factors and limited collection on image-caricature pair data. Thus, this study proposes facial caricature dataset preparation process to get good quality dataset which captures the artist’s exaggeration style and a hybrid model to generalize the inconsistent style so that a better, more accurate prediction can be obtained even using small amount of dataset. The proposed data preparation process involves facial features parameter extraction based on landmark-based geometric morphometric and modified data normalization method based on Procrustes superimposition method. The proposed hybrid model (BP-GANN) combines Backpropagation Neural Network (BPNN) and Genetic Algorithm Neural Network (GANN). The experimental result shows that the proposed hybrid BP-GANN model is outperform the traditional hybrid GA-BPNN model, individual BPNN model and individual GANN model. The modified Procrustes superimposition method also produces a better quality dataset than the original one. 2017 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/78996/1/SuriatiSadimonPFC2017.pdf Sadimon, Suriati (2017) Hybrid learning-based model for exaggeration style of facial caricature. PhD thesis, Universiti Teknologi Malaysia, Faculty of Computing. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:107415
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sadimon, Suriati
Hybrid learning-based model for exaggeration style of facial caricature
description Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeration style of an artist is difficult to be coded in algorithmic method. Fortunately, artificial neural network, which possesses self-learning and generalization ability, has shown great promise in addressing the problem of capturing and learning an artist’s style to predict a facial caricature. However, one of the main issues faced by this study is inconsistent artist style due to human factors and limited collection on image-caricature pair data. Thus, this study proposes facial caricature dataset preparation process to get good quality dataset which captures the artist’s exaggeration style and a hybrid model to generalize the inconsistent style so that a better, more accurate prediction can be obtained even using small amount of dataset. The proposed data preparation process involves facial features parameter extraction based on landmark-based geometric morphometric and modified data normalization method based on Procrustes superimposition method. The proposed hybrid model (BP-GANN) combines Backpropagation Neural Network (BPNN) and Genetic Algorithm Neural Network (GANN). The experimental result shows that the proposed hybrid BP-GANN model is outperform the traditional hybrid GA-BPNN model, individual BPNN model and individual GANN model. The modified Procrustes superimposition method also produces a better quality dataset than the original one.
format Thesis
author Sadimon, Suriati
author_facet Sadimon, Suriati
author_sort Sadimon, Suriati
title Hybrid learning-based model for exaggeration style of facial caricature
title_short Hybrid learning-based model for exaggeration style of facial caricature
title_full Hybrid learning-based model for exaggeration style of facial caricature
title_fullStr Hybrid learning-based model for exaggeration style of facial caricature
title_full_unstemmed Hybrid learning-based model for exaggeration style of facial caricature
title_sort hybrid learning-based model for exaggeration style of facial caricature
publishDate 2017
url http://eprints.utm.my/id/eprint/78996/1/SuriatiSadimonPFC2017.pdf
http://eprints.utm.my/id/eprint/78996/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:107415
_version_ 1643658068368556032
score 13.211869

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