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J. Imaging 2019, 5(1), 2; https://doi.org/10.3390/jimaging5010002

What’s in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance

1
School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, UK
2
School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
3
Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, FI-90014 Oulu, Finland
4
Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, FI-90014 Oulu, Finland
This article is an extended version of our paper published in Farnell, D.J.J.; Galloway, J.; Zhurov, A.; Richmond, S.; Pirttiniemi, P.; Lähdesmäki, R. What’s in a Smile? Initial Results of Multilevel Principal Components Analysis of Facial Shape and Image Texture. In Medical Image Understanding and Analysis; Springer: Cham, Switzerland, 2018; Volume 894, pp. 177–188.
*
Author to whom correspondence should be addressed.
Received: 15 November 2018 / Revised: 13 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
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Abstract

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”. View Full-Text
Keywords: multilevel principal components analysis; shape and image texture; facial expression multilevel principal components analysis; shape and image texture; facial expression
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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 (CC BY 4.0).
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Farnell, D.J.J.; Galloway, J.; Zhurov, A.I.; Richmond, S.; Marshall, D.; Rosin, P.L.; Al-Meyah, K.; Pirttiniemi, P.; Lähdesmäki, R. What’s in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance. J. Imaging 2019, 5, 2.

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