Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F18%3A00326473" target="_blank" >RIV/68407700:21460/18:00326473 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/HealthCom.2018.8531195" target="_blank" >http://dx.doi.org/10.1109/HealthCom.2018.8531195</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/HealthCom.2018.8531195" target="_blank" >10.1109/HealthCom.2018.8531195</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis

Popis výsledku v původním jazyce

Many current studies conclude that facial attractiveness perception is data-based and irrespective of the perceiver. However, analyses of facial geometric image data and its visual impact always exceeded power of classical statistical methods. In this study, we have applied machine-learning methods to identify geometric features of a face associated with an increase of facial attractiveness after undergoing rhinoplasty. Furthermore, we explored how accurate classification of faces into sets of facial emotions and their facial manifestations is, since categorization of human faces into emotions manifestation should take into consideration the fact that total face impression is also dependent on expressed facial emotion. Both profile and portrait facial image data were collected for each patient (n = 42), processed, landmarked and analysed using R language. Multivariate linear regression was performed to select predictors increasing facial attractiveness after undergoing rhinoplasty. The sets of used facial emotions originate from Ekman-Friesen FACS scale, but was improved substantially. Bayesian naive classifiers, decision trees (CART) and neural networks were learned to allow assigning a new face image data into one of facial emotions. Enlargements of both a nasolabial and nasofrontal angle within rhinoplasty were determined as significant predictors increasing facial attractiveness (p < 0.05). Neural networks manifested the highest predictive accuracy of a new face classification into facial emotions. Geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order final classified emotion, as was identified using decision trees. We performed machine-learning analyses to point out which facial geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be treated within plastic surgeries.

Název v anglickém jazyce

Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis

Popis výsledku anglicky

Many current studies conclude that facial attractiveness perception is data-based and irrespective of the perceiver. However, analyses of facial geometric image data and its visual impact always exceeded power of classical statistical methods. In this study, we have applied machine-learning methods to identify geometric features of a face associated with an increase of facial attractiveness after undergoing rhinoplasty. Furthermore, we explored how accurate classification of faces into sets of facial emotions and their facial manifestations is, since categorization of human faces into emotions manifestation should take into consideration the fact that total face impression is also dependent on expressed facial emotion. Both profile and portrait facial image data were collected for each patient (n = 42), processed, landmarked and analysed using R language. Multivariate linear regression was performed to select predictors increasing facial attractiveness after undergoing rhinoplasty. The sets of used facial emotions originate from Ekman-Friesen FACS scale, but was improved substantially. Bayesian naive classifiers, decision trees (CART) and neural networks were learned to allow assigning a new face image data into one of facial emotions. Enlargements of both a nasolabial and nasofrontal angle within rhinoplasty were determined as significant predictors increasing facial attractiveness (p < 0.05). Neural networks manifested the highest predictive accuracy of a new face classification into facial emotions. Geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order final classified emotion, as was identified using decision trees. We performed machine-learning analyses to point out which facial geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be treated within plastic surgeries.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)

ISBN

978-1-5386-4294-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Ostrava

Datum konání akce

17. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—