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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Article name in the collection

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

ISBN

978-1-5386-4294-8

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

1-6

Publisher name

IEEE

Place of publication

Piscataway

Event location

Ostrava

Event date

Sep 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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