Machine-learning at the service of plastic surgery: A case study evaluating facial attractiveness and emotions using R language
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine-learning at the service of plastic surgery: A case study evaluating facial attractiveness and emotions using R language
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Federated Conference on Computer Science and Information Systems
ISBN
9788395541605
ISSN
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e-ISSN
2300-5963
Number of pages
6
Pages from-to
107-112
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
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Event location
Leipzig
Event date
Sep 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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