skip to main content
10.1145/3472813.3472818acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicmhiConference Proceedingsconference-collections
research-article

A Machine Learning approach to study soft-tissue facial characteristics as indicators of woman attractiveness

Published: 26 October 2021 Publication History

Abstract

The canons of beauty have undergone substantial changes over the years. Social, cultural and environmental factors influence the perception of beauty. For the face these considerations become even more important, being the key of all social interactions. Several studies have been carried out in this field, to try to give objectivity to these aspects. In this work, starting from a dataset consisting of linear and angular photogrammetric measurements of the faces of 65 women from two different groups, Machine Learning algorithms were trained and tested for the automatic classification of the two groups of individuals. Results were then compared and the predictive power of the adopted classifiers was discussed in terms of sensitivity and specificity.

Supplementary Material

Presentation slides (p22-d-alessio-supplement.pptx)

References

[1]
Fadeev R.A., Izpravnikova A.N. Results of expert evaluation of violations of facial aesthetics in various forms of dental anomalies. Institut stomatologii. 2009;45(4):21–25.
[2]
Solov'ev M.M., Katinas E.B., An I.A. Pattern of facial beauty: previously undefined proportions. Russian Medical Inquiry. 2020;4(4):226–232.
[3]
Auger T.A., Turley P.K. The female soft tissue profile as presented in fashion magazines during the 1900s: a photographic analysis. Int J Adult Orthodon Orthognath Surg. 1999;14(1):7–18.
[4]
Arnett G.W., Bergman R.T. Facial keys to orthodontic diagnosis and treatment planning. Part I. Am J Orthod Dentofacial Orthop. 1993; 103(4):299–312. (93) 70010-L.
[5]
Celebi, Ahmet Arif DDS, PhD∗; Kau, Chung How DDS, PhD∗; Femiano, Felice MD, PhD†; Bucci, Ludovica†; Perillo, Letizia MD, PhD† A Three-Dimensional Anthropometric Evaluation of Facial Morphology, Journal of Craniofacial Surgery: March 2018 - Volume 29 - Issue 2 - p 304-308
[6]
M.A. Neger, A quantitative method for the evaluation of the soft tissue facial profile. Am J Orthod, 45 (1959), pp. 738-751
[7]
L.L. Merrifield, The profile line as an aid in critically evaluating facial esthetics. Am J Orthod, 52 (11) (1966), pp. 804-822
[8]
Galantucci LM, Ferrandes R, Percoco G (2006) Digital Photogrammetry for Facial Recognition. J Comput Inf Sci Eng 6: 390–396.
[9]
Böhringer S, de Jong MA (2019) Quantification of Facial Traits. Front Genet 10.
[10]
Lo L-J, Yang C-T, Ho C-T, (2021) Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model: A Preliminary Experience. Ann Plast Surg 86: S224.
[11]
Lee H-B, Lee S-H, Kim J-S, (2010) Evaluation of Influence of Individual Facial Aesthetic Subunits on the Congnition of Facial Attractiveness in Public. Arch Plast Surg 37: 361–368.
[12]
Rao GKL, Srinivasa AC, Iskandar YHP, (2019) Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol 9: 715–724.
[13]
Improta G, Ponsiglione AM, Parente G, (2020) Evaluation of Medical Training Courses Satisfaction: Qualitative Analysis and Analytic Hierarchy Process. In: Jarm T, Cvetkoska A, Mahnič-Kalamiza S, (Eds.), 8th European Medical and Biological Engineering Conference, Cham, Springer International Publishing, 518-526.
[14]
Ricciardi C, Cantoni V, Improta G, (2020) Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center. Comput Methods Programs Biomed 189.
[15]
Ricciardi C, Ponsiglione AM, Converso G, (2021) Implementation and validation of a new method to model voluntary departures from emergency departments. Math Biosci Eng 18: 253–273.
[16]
Trunfio TA, Scala A, Vecchia AD, (2021) Multiple Regression Model to Predict Length of Hospital Stay for Patients Undergoing Femur Fracture Surgery at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital, In: Jarm T, Cvetkoska A, Mahnič-Kalamiza S, (Eds.), 8th European Medical and Biological Engineering Conference, Cham, Springer International Publishing, 840–847.
[17]
Cabitza F, Locoro A, Banfi G (2018) Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol 6.
[18]
Hachesu PR, Ahmadi M, Alizadeh S, (2013) Use of data mining techniques to determine and predict length of stay of cardiac patients. Healthc Inform Res 19: 121–129.
[19]
Sodhro AH, Sangaiah AK, Sodhro GH, (2018) An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors 18: 923.
[20]
Romano M, D'Addio G, Clemente F, (2014) Symbolic dynamic and frequency analysis in foetal monitoring, IEEE Computer Society.
[21]
Cesarelli M, Romano M, Bifulco P, Improta G, D'Addio G. An application of symbolic dynamics for FHRV assessment. Stud Health Technol Inform. 2012;180:123-7.
[22]
Jeon G, Ahmad A, Cuomo S, (2019) Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics. J Ambient Intell Humaniz Comput 10: 3739–3745
[23]
Moore CS, Wood TJ, Beavis AW, (2013) Correlation of the clinical and physical image quality in chest radiography for average adults with a computed radiography imaging system. Br J Radiol 86: 20130077.
[24]
Parmar C, Grossmann P, Bussink J, (2015) Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep 5: 13087.
[25]
Hossain MS (2016) Patient State Recognition System for Healthcare Using Speech and Facial Expressions. J Med Syst 40: 272.
[26]
Romeo V, Cuocolo R, Ricciardi C, (2020) Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach. Anticancer Res. 40(1):271-280.
[27]
Wuerpel E.H. On facial balance and harmony. Angle Orthod. 1937; 7:81–89.
[28]
Sforza C, Laino A, D'Alessio R, (2009) Soft-tissue facial characteristics of attractive Italian women as compared to normal women. Angle Orthod 79: 17–23.
[29]
Galantucci LM, Percoco G, Lavecchia F (2013) A New Three-Dimensional Photogrammetric Face Scanner for the Morpho-Biometric 3D Feature Extraction Applied to a Massive Field Analysis of Italian Attractive Women. Procedia CIRP 5: 259–264.
[30]
Deli R, Galantucci LM, Laino A, (2013) Three-dimensional methodology for photogrammetric acquisition of the soft tissues of the face: a new clinical-instrumental protocol. Prog Orthod 14.
[31]
Dindaroğlu F, Kutlu P, Duran GS, (2016) Accuracy and reliability of 3D stereophotogrammetry: A comparison to direct anthropometry and 2D photogrammetry. Angle Orthod 86: 487–494

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICMHI '21: Proceedings of the 5th International Conference on Medical and Health Informatics
May 2021
347 pages
ISBN:9781450389846
DOI:10.1145/3472813
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2021

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICMHI 2021

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 32
    Total Downloads
  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 23 Jan 2025

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media