Lubomír Štěpánek
M.Sc. and Ph.D. in Statistics, M.D. in General Medicine, Biostatistician, Software Developer, Assistant Professor at Charles University & Prague University of Economics and Business
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I hold M.Sc. and Ph.D. degrees in Statistics, an M.D. in General Medicine, and I'm pursuing a Ph.D. in Biomedical Informatics. As an assistant professor at Charles University and Prague University of Economics and Business, I specialize in survival analysis, machine learning, computational psychometrics, and robust statistics. I'm passionate about R and LaTeX, and I teach statistics, maths, survival and R programming courses. I'm also a biostatistician consulting research papers' statistics.
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A shiny application enabling facial attractiveness evaluation for purposes of plastic surgery
The ways how to evaluate facial attractiveness complexly and how to make comparisons between facial images of patients before and after facial plastic surgery procedure are still unclear and require ongoing research.
In this study, we have developed a web-based shiny application providing facial image processing, both manual and automated landmarking, facial geometry computations and machine-learning models allowing to identify geometric facial features associated with an increase of facial attractiveness after undergoing rhinoplasty, common facial plastic surgery.
Patients’ facial image data were processed, landmarked and analysed using the application. Facial attractiveness was measured using Likert scale by a board of independent observers. Machine-learning built-in approaches were performed to select predictors increasing facial attractiveness after undergoing rhinoplasty.
The shiny web framework enables to develop a complex web interface including HTML, CSS and javascript front-end and R-based back-end bridging C++ library dlib which performs image computations. In addition, the connected shinyjs package offers a user-server clickable interaction useful for the landmarking.
keywords: shiny, R, machine learning, facial attractiveness, plastic surgery
Machine-learning and R in plastic surgery -- Classification and attractiveness of facial emotions
Plenty of current studies conclude that human facial attractiveness perception is data-based and irrespective of the perceiver. However, the ways how to analyse associations between facial geometric image data and its visual impact always exceeded the power of classical statistical methods. What is more, current plastic surgery deals with aesthetic indications such as an improvement of the attractiveness of a smile or other facial emotions, therefore it should take into consideration the fact that total face impression is also dependent on presently expressed facial emotion.
In this work, we have applied machine-learning methods and a power of R language (and some of its packages) to explore how accurate classification of photographed faces into sets of facial emotions and their facial manifestations is, and – furthermore – which facial emotions are associated with higher level of facial attractiveness, measured using Likert scale by a board of independent observers.
Both profile and portrait facial image data were collected for each of a patient (exposed to an emotion incentive), then processed, landmarked and analysed using R language. The sets of used facial emotions and other facial manifestation originate from Ekman-Friesen FACS scale but were improved substantially. Bayesian naive classifiers using e1071 package, decision trees (CART) via tree and rpart packages and, finally, neural networks by neural net package were learned to allow assigning a new face image data into one of the facial emotions.
Neural networks manifested the highest predictive accuracy of a new face categorization into facial emotions. The geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order an intensity of a classified emotion, as was identified using decision trees. The mentioned R packages proved their maturity.
We performed machine-learning analyses to compare which one of classification methods, implemented via R packages, conducts the best prediction accuracy when classifying face images into facial emotions, and – additionally – to point out which facial emotions and geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be addressed within plastic surgery procedures.
EHB 2019
My talk was followed the name "Evaluation of Facial Attractiveness after Undergoing Rhinoplasty Using Tree-based and Regression Methods".
Why R? 2019 Conference
I named my work as "Facial landmarking made (possible and) easy with R!".
ISAT 2019
I gave an oral talk there with a name "Machine-learning and R in plastic surgery – Evaluation of facial attractiveness and classification of facial emotions".
FedCSIS 2019
I gave an oral talk there named "Machine-learning at the service of plastic surgery: a case study evaluating facial attractiveness and emotions using R language".
SatRday Gdansk 2019 Sessionize Event
Psychoco 2019
The aim of this workshop was to bring together researchers from statistics, psychology, and related disciplines working on modern techniques for the analysis of data from psychology and the social sciences. Following the fruitful previous workshops, it provided a platform for discussions about implementation and application of software on the interface of statistical inference, computational methods, and applied psychometrics. A special emphasis is given to implementations in the R system for statistical computing. To create a diverse and inspiring program, both authors and users of psychometric software packages were encouraged to present their recent and ongoing work.
I gave there an oral talk dedicated to various generalizations of the Upper-Lower Index and their critical values.
satRday Belgrade 2018 Sessionize Event
useR! 2018
useR! is the main meeting of the R user and developer community, its program consisting of both invited and user-contributed presentations. The invited keynote lectures cover a broad spectrum of topics ranging from technical and R-related computing issues to general statistical topics of current interest. The user-contributed presentations are submitted as abstracts prior to the conference and may be related to (virtually) any R-related topic. The presentations are typically organized in sessions of either broad or special interest, which also comprise a “free” discussion format. Such a discussion format not only provides a forum for software demonstrations and detailed discussions but also supports the self-organization of the respective communities.
In 2018, the useR! conference was held in beautiful Brisbane and I gave there a regular oral talk on classification and evaluation of facial attractiveness and emotions for purposes of plastic surgery using machine-learning methods and R.
Lubomír Štěpánek
M.Sc. and Ph.D. in Statistics, M.D. in General Medicine, Biostatistician, Software Developer, Assistant Professor at Charles University & Prague University of Economics and Business
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