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. https://doi.org/10.3390/jimaging5010002
What’s in a smile? : initial analyses of dynamic changes in facial shape and appearance
|Author:||Farnell, Damian J. J.1; Galloway, Jennifer1; Zhurov, Alexei I.1;|
1School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, UK
2School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
3Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, FI-90014 Oulu, Finland
4Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, FI-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003198575
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2020-03-19
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?”.
Journal of imaging
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
NFBC1966 received financial support from University of Oulu, grant no. 24000692; Oulu University Hospital, grant no. 24301140; ERDF European Regional Development Fund, grant no. 539/2010 A31592.
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).