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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21460/20:00336672

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30212 - Surgery

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

10

Strana od-do

243-252

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Wroclaw

Datum konání akce

15. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000564742800022