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A Multiscale Constraints Method Localization of 3D Facial Feature Points

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative...

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Detalles Bibliográficos
Autores principales: Li, Hong-an, Zhang, Yongxin, Li, Zhanli, Li, Huilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4620036/
https://www.ncbi.nlm.nih.gov/pubmed/26539244
http://dx.doi.org/10.1155/2015/178102
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author Li, Hong-an
Zhang, Yongxin
Li, Zhanli
Li, Huilin
author_facet Li, Hong-an
Zhang, Yongxin
Li, Zhanli
Li, Huilin
author_sort Li, Hong-an
collection PubMed
description It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.
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spelling pubmed-46200362015-11-04 A Multiscale Constraints Method Localization of 3D Facial Feature Points Li, Hong-an Zhang, Yongxin Li, Zhanli Li, Huilin Comput Math Methods Med Research Article It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms. Hindawi Publishing Corporation 2015 2015-10-11 /pmc/articles/PMC4620036/ /pubmed/26539244 http://dx.doi.org/10.1155/2015/178102 Text en Copyright © 2015 Hong-an Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Hong-an
Zhang, Yongxin
Li, Zhanli
Li, Huilin
A Multiscale Constraints Method Localization of 3D Facial Feature Points
title A Multiscale Constraints Method Localization of 3D Facial Feature Points
title_full A Multiscale Constraints Method Localization of 3D Facial Feature Points
title_fullStr A Multiscale Constraints Method Localization of 3D Facial Feature Points
title_full_unstemmed A Multiscale Constraints Method Localization of 3D Facial Feature Points
title_short A Multiscale Constraints Method Localization of 3D Facial Feature Points
title_sort multiscale constraints method localization of 3d facial feature points
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4620036/
https://www.ncbi.nlm.nih.gov/pubmed/26539244
http://dx.doi.org/10.1155/2015/178102
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