Abstract
Although facial attractiveness is data-driven and nondependent on a perceiver, traditional statistical methods cannot properly identify relationships between facial geometry and its visual impression. Similarly, classification of facial images into facial emotions is also challenging, since the classification should consider the fact that overall facial impression is always dependent on currently present facial emotion.
To address the problems, both profile and portrait facial images of the patients (\( n = 42 \)) were preprocessed, landmarked, and analyzed via R language. Multivariate regression was carried out to detect indicators increasing facial attractiveness after going through rhinoplasty.
Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were built to classify a new facial image into one of the facial emotions, defined using Ekman-Friesen FACS scale.
Nasolabial and nasofrontal angles’ enlargement within rhinoplasty increases facial attractiveness (\( p < 0.05 \)). Decision trees proved the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks returned the highest accuracy of the classification.
Performed machine-learning analyses pointed out which facial features affect facial attractiveness the most and should be therefore treated by plastics surgery procedures. The classification of facial images into emotions show possible associations between facial geometry and facial emotions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farkas, L.G., Hreczko, T.A., Kolar, J.C., Munro, I.R.: Vertical and horizontal proportions of the face in young adult North American Caucasians. Plast. Reconstr. Surg. 75(3), 328–338 (1985)
Schmid, K., Marx, D., Samal, A.: Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recogn. 41, 2710–2717 (2008)
Bashour, M.: Is an Objective Measuring System for Facial Attractiveness Possible, 1st edn., Boca Raton, Florida (2007). ISBN 978-158-1123-654
Thornhill, R., Gangestad, S.W.: Human facial beauty. Hum. Nature 4, 237–269 (1993)
Little, A.C., Jones, B.C., DeBruine, L.M.: Facial attractiveness: evolutionary based research. Philos. Trans. R. Soc. B Biol. Sci. 366, 1638–1659 (2011)
Perrett, D.I., Lee, J.K., Penton-Voak, I., Rowland, D., Yoshikawa, S., Burt, D.M., Henzi, S.P., Castles, D.L., Akamatsu, S.: Effects of sexual dimorphism on facial attractiveness. Nature 394(6696), 884–887 (1998)
Naini, F.: Facial Aesthetics: Concepts & Clinical Diagnosis, 1st edn. Wiley-Blackwell, Chichester (2011). ISBN 978-1-405-18192-1
Darwin, C., Ekman, P., Prodger, P.: The Expression of the Emotions in Man and Animals, 1st edn. Oxford University Press (1998). ISBN 9780195158069
Tomkins, S.: Affect Imagery Consciousness, 1st edn. Springer, New York (1963). ISBN 0826144047
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17, 124–129 (1971)
Ekman, P.: Unmasking the Face: A Guide to Recognizing Emotions from Facial Clues. Malor Books, Cambridge (2003). ISBN 1883536367
Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recogn. 36(1), 259–278 (2003)
Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002). ISSN 0162-8828
Lanitis, A., Taylor, C., Cootes, T.: Automatic face identification system using flexible appearance models. Image Vis. Comput. 13, 393–401 (1995)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998). ISSN 0162-8828
Zhao, X., Zhang, S.: A review on facial expression recognition: feature extraction and classification. IETE Tech. Rev. 33, 505–517 (2016)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Alpaydin, E.: Introduction to Machine Learning, 2nd edn. MIT Press, Cambridge (2010). ISBN 9780262012430
Kasal, P., Fiala, P., Stepanek, L., Mestak, J.: Application of image analysis for clinical evaluation of facial structures. Medsoft 2015, 64–70 (2015)
Stepanek, L., Kasal, P., Mestak, J.: Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis. In: 20th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2018, Ostrava, Czech Republic, 17–20 September 2018, pp. 1–6 (2018). https://doi.org/10.1109/HealthCom.2018.8531195
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013). http://www.R-project.org/. ISBN 3-900051-07-0
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Chambers, J.: Statistical Models in S. Chapman & Hall/CRC, Boca Raton (1992). ISBN 041283040X
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997). ISSN 0885-6125
Breiman, L.: Classification and Regression Trees. Chapman & Hall, New York (1993). ISBN 0412048418
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multiclass classification competition. In: The 2011 International Joint Conference on Neural Networks, pp. 1453–1460 (2011). https://doi.org/10.1109/ijcnn.2011.6033395
Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., Poggio, T., Gerald, W., Loda, M., Lander, E.S., Golub, T.R.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. 98, 15149–15154 (2001). ISSN 0027-8424
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Štěpánek, L., Kasal, P., Měšťák, J. (2020). Machine-Learning and R in Plastic Surgery – Evaluation of Facial Attractiveness and Classification of Facial Emotions. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-30604-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-30604-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30603-8
Online ISBN: 978-3-030-30604-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)