Machine-Learning and R in Plastic Surgery - Evaluation of Facial Attractiveness and Classification of Facial Emotions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F20%3A10414956" target="_blank" >RIV/00216208:11110/20:10414956 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/20:00336672

Result on the web

<a href="https://doi.org/10.1007/978-3-030-30604-5_22" target="_blank" >https://doi.org/10.1007/978-3-030-30604-5_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-30604-5_22" target="_blank" >10.1007/978-3-030-30604-5_22</a>

Result language

angličtina

Original language name

Machine-Learning and R in Plastic Surgery - Evaluation of Facial Attractiveness and Classification of Facial Emotions

Original language description

Although facial attractiveness is data-driven and nondependent on a perceiver, traditional statistical methods cannot properly identify relationships between facial geometry and its visual impression. Similarly, classification of facial images into facial emotions is also challenging, since the classification should consider the fact that overall facial impression is always dependent on currently present facial emotion. To address the problems, both profile and portrait facial images of the patients (n = 42) were preprocessed, landmarked, and analyzed via R language. Multivariate regression was carried out to detect indicators increasing facial attractiveness after going through rhinoplasty. Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were built to classify a new facial image into one of the facial emotions, defined using Ekman-Friesen FACS scale. Nasolabial and nasofrontal angles&apos; enlargement within rhinoplasty increases facial attractiveness (p&lt;0.05). Decision trees proved the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks returned the highest accuracy of the classification. Performed machine-learning analyses pointed out which facial features affect facial attractiveness the most and should be therefore treated by plastics surgery procedures. The classification of facial images into emotions show possible associations between facial geometry and facial emotions.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30212 - Surgery

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Information systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology - ISAT 2019. Part II

ISBN

978-3-030-30603-8

ISSN

2194-5357

e-ISSN

2194-5365

Number of pages

10

Pages from-to

243-252

Publisher name

Springer

Place of publication

Cham

Event location

Wroclaw

Event date

Sep 15, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000564742800022