Machine-learning at the service of plastic surgery: A case study evaluating facial attractiveness and emotions using R language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F19%3A00336673" target="_blank" >RIV/68407700:21460/19:00336673 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8860002" target="_blank" >https://ieeexplore.ieee.org/document/8860002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2019F264" target="_blank" >10.15439/2019F264</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine-learning at the service of plastic surgery: A case study evaluating facial attractiveness and emotions using R language

Popis výsledku v původním jazyce

Since the plastic surgery should consider that facial impression is always dependent on current facial emotion, it came to be verified how precise classification of facial images into sets of defined facial emotions is.Multivariate regression was performed using R language to identify indicators increasing facial attractiveness after undergoing rhinoplasty. Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were applied to assign a landmarked facial image data into one of the facial emotions, based on Ekman-Friesen FACS scale.Enlargement of nasolabial and nasofrontal angle within rhinoplasty significantly predicts facial attractiveness increasing (p<; 0.05). Decision trees showed the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks proved the highest accuracy of the classification.Performed machine-learning analyses pointed out which geometric facial features increase facial attractiveness the most and should be consequently treated by plastic surgeries.

Název v anglickém jazyce

Machine-learning at the service of plastic surgery: A case study evaluating facial attractiveness and emotions using R language

Popis výsledku anglicky

Since the plastic surgery should consider that facial impression is always dependent on current facial emotion, it came to be verified how precise classification of facial images into sets of defined facial emotions is.Multivariate regression was performed using R language to identify indicators increasing facial attractiveness after undergoing rhinoplasty. Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were applied to assign a landmarked facial image data into one of the facial emotions, based on Ekman-Friesen FACS scale.Enlargement of nasolabial and nasofrontal angle within rhinoplasty significantly predicts facial attractiveness increasing (p<; 0.05). Decision trees showed the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks proved the highest accuracy of the classification.Performed machine-learning analyses pointed out which geometric facial features increase facial attractiveness the most and should be consequently treated by plastic surgeries.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Federated Conference on Computer Science and Information Systems

ISBN

9788395541605

ISSN

—

e-ISSN

2300-5963

Počet stran výsledku

6

Strana od-do

107-112

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

—

Místo konání akce

Leipzig

Datum konání akce

1. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—