Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Damian Farnell
* ORCID logo ,
Jennifer Galloway
,
Alexei Zurov
,
Stephen Richmond
,
David Marshall
,
Paul Rosin
,
Khtam Al-Meyah
,
Pertti Pirttiniemi
,
Raija Lähdesmäki
ORCID logo
Version 1 : Received: 15 November 2018 / Approved: 19 November 2018 / Online: 19 November 2018 (04:57:36 CET)

A peer-reviewed article of this Preprint also exists.

Farnell, D.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. Farnell, D.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.

Abstract

Single-level Principal Components 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?”

Keywords

multilevel principal components analysis; shape and image texture; facial expression

Subject

Medicine and Pharmacology, Dentistry and Oral Surgery

Copyright: 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.

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