Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering

Statistical Shape Analysis for 3D Facial Images

Authors
M. Nakatsu, X.H. Han, R. Kimura, Y.W. Chen
Corresponding Author
M. Nakatsu
Available Online July 2015.
DOI
10.2991/aiie-15.2015.94How to use a DOI?
Keywords
facial morphology; gene; statistical learning; mean hyperplane; normalization
Abstract

Recently, the genetic association of human facial morphological variation attracts substantial attention. This study proposes a general framework for analyzing facial morphology variation using scanned 3D landmarks, and explores the phenotype features of facial morphology for identifying population root of Japanese archipelago. After registration for the dense 3D facial points, we investigate both PCA and Mean Hyperplane for exploring the facial morphological variations. Then, in order to reduce the in-population variance of statistical features, we normalize them firstly, and explore the identification of population using the combined phenotype features. Experiments show that our proposed strategy can give promising identification performances between the Mainland Japanese and the Ryukyuan.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
10.2991/aiie-15.2015.94
ISSN
1951-6851
DOI
10.2991/aiie-15.2015.94How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - M. Nakatsu
AU  - X.H. Han
AU  - R. Kimura
AU  - Y.W. Chen
PY  - 2015/07
DA  - 2015/07
TI  - Statistical Shape Analysis for 3D Facial Images
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 337
EP  - 340
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.94
DO  - 10.2991/aiie-15.2015.94
ID  - Nakatsu2015/07
ER  -