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.