Abstract
In the literature, several papers report studies on mathematical models used to describe facial features and to predict female facial beauty based on 3D human face data. Many authors have proposed the principal component analysis (PCA) method that permits modeling of the entire human face using a limited number of parameters. In some cases, these models have been correlated with beauty classifications, obtaining good attractiveness predictability using wrapped 2D or 3D models. To verify these results, in this paper, the authors conducted a three-dimensional digitization study of 66 very attractive female subjects using a computerized noninvasive tool known as 3D digital photogrammetry. The sample consisted of the 64 contestants of the final phase of the Miss Italy 2010 beauty contest, plus the two highest ranked contestants in the 2009 competition. PCA was conducted on this real faces sample to verify if there is a correlation between ranking and the principal components of the face models. There was no correlation and therefore, this hypothesis is not confirmed for our sample. Considering that the results of the contest are not only solely a function of facial attractiveness, but undoubtedly are significantly impacted by it, the authors based on their experience and real faces conclude that PCA analysis is not a valid prediction tool for attractiveness. The database of the features belonging to the sample analyzed are downloadable online and further contributions are welcome.
Similar content being viewed by others
References
Alley TR, Cunningham MR (1991) Average faces are attractive, but very attractive faces are not average. Psychol Sci 2:123–125
Cunningham MR (1986) Measuring the physical in physical attractiveness. quasi-experiments on the sociobiology of female facial beauty. J Pers Soc Psychol 50:925–935
De Menezes M, Riccardo Rosati R, Allievi C, Sforza C (2009) A photographic system for the three-dimensional study of facial morphology. Angle Orthod 79:1070–1077
Deli R, Di Gioia E, Galantucci LM, Percoco G (2010) Automated landmarks extraction for orthodontic measurement of faces using the 3 cameras photogrammetry methodology. J Craniofac Surg 21:87–93
Deli R, Di Gioia E, Galantucci LM, Percoco G (2011) Accurate facial morphology measurements using a 3-camera photogrammetric method. J Craniofac Surg 22:54–59
Deli R, Galantucci LM, Laino A, D’Alessio R, Di Gioia E, Savastano C, Lavecchia F, Percoco G (2013) Three-dimensional methodology for photogrammetric acquisition of the soft tissues of the face: a new clinical-instrumental protocol. Prog Orthod 14:32
Dryden IL, Mardia KV (1993) Multivariate shape analysis. Sankhya 55:460–480
Dryden IL, Mardia KV (1998) Statistical shape analysis. Wiley, Chichester
Edler RJ (2001) Background considerations to facial aesthetics. J Orthod 28:159–168
Efraty B, Bilgazyev E, Shah S, Kakadiaris IA (2012) Profile-based 3D-aided face recognition. Pattern Recogn 45:43–53
Fan J, Chau KP, Wan X, Zhai L, Lau E (2012) Prediction of facial attractiveness from facial proportions. Pattern Recogn 45:2326–2334
Farkas LG (1981) Anthropometry of the head and face. Elsevier, New York
Farkas LG, Munro IR (1987) Anthropometric facial proportions in medicine. Charles C. Thomas, Illinois
Ferrario VF, Sforza C, Poggio CE, Tartaglia LG (1995) Facial morphometry of television actresses compared with normal women. J Oral Maxillofac Surg 53:1008–1015
Galantucci LM (2010) New challenges for reverse engineering in facial treatments: how can the new 3D non-invasive surface measures support diagnoses and cures? Virtual Phys Prototyp 5:3–12
Galantucci LM, Ferrandes R, Percoco G (2006) Digital photogrammetry for facial recognition. J Comput Inf Sci Eng 6:390–396
Galantucci LM, Percoco G, Dal Maso U (2008) Coded targets and hybrid grids for photogrammetric 3D digitisation of human faces. Virtual Phys Prototyp 3:167–176
Galantucci LM, Percoco G, Di Gioia E (2010) Low cost 3D face scanning based on landmarks and photogrammetry: a new tool for a surface diagnosis in orthodontics. In: Ao S, Castillo O, Huang X (eds) Intelligent automation and computer engineering, lecture notes in electrical engineering, intelligent automation and computer engineering series, vol 52. Springer, Berlin, pp 93–106
Galantucci LM, Percoco G, Di Gioia E (2012) New 3D digitizer for human faces based on digital close range photogrammetry: application to face symmetry analysis. JDCTA 6:703–713
Galantucci LM, Percoco G, Lavecchia F, Di Gioia E (2013) Non-invasive computerized scanning method for the correlation between the facial soft and hard tissues for an integrated 3D anthropometry and cephalometry. J Craniofac Surg 24:797–804
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
Galton F (1879) Composite portraits, made by combining those of many different persons in a single resultant figure. J Anthropol Inst Great Br Irel 8:132–144
Goodall C (1991) Procrustes methods in the statistical analysis of shape. J R Stat Soc Ser B (Methodol) 53:285–339
Gower JC (1975) Generalized procrustes analysis. Psychometrika 40:33–51
Hönn M, Göz G (2007) The ideal of facial beauty: a review. J Orofac Orthop 68:6–16
Kendall DG (1984) Shape manifolds, procrustean metrics, and complex projective spaces. Bull Lond Math Soc 16:81–121
Koury ME, Epker BN, Turvey TA (1992) Maxillofacial esthetics: anthropometrics of the maxillofacial region. Int J Oral Maxillofac Surg 50:806–820
Naini FB, Moss JP, Gill DS (2006) The enigma of facial beauty: esthetics, proportions, deformity, and controversy. Am J Orthod Dentofacial Orthop 130:277–282
O’Higgins P, Jones N (1998) Facial growth in Cercocebus torquatus: an application of three dimensional geometric morphometric techniques to the study of morphological variation. J Anat 193:251–272
Pearson product-moment correlation coefficient. From Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient. Accessed on 13 June 2013
Peck H, Peck S (1970) A concept of facial esthetics. Esthetics 40:284–318
Powell N, Humphries B (1984) Proportions of the aesthetic face. C. M. Thieme-Stratton Inc., New York
Rohlf FJ, Slice DE (1990) Extensions of the procrustes method for the optimal superimposition of landmarks. Syst Zool 39:40–59
Sforza C, Laino A, D’Alessio R, Dellavia C, Grandi G, Ferrario VF (2007) Three-dimensional facial morphometry of attractive children and normal children in the deciduous and early mixed dentition. Angle Orthod 77:1025–1033
Sforza C, Laino A, D’Alessio R, Grandi G, Dellavia C, Tartaglia GM, Ferrario VF (2007) Three-dimensional facial morphometry of attractive Italian women. Prog Orthod 8:282–293
Sforza C, Laino A, D’Alessio R, Grandi G, Tartaglia GM, Ferrario VF (2008) Soft-tissue facial characteristics of attractive and normal adolescent boys and girls. Angle Orthod 78:799–807
Sforza C, Laino A, D’Alessio R, Grandi G, Binelli M, Ferrario VF (2009) Soft-tissue facial characteristics of attractive Italian women as compared to normal women. Angle Ortho 79:17–23
Symons D (1979) The evolution of human sexuality. Oxford University Press, New York
Valenzano DR, Mennucci A, Tartarelli G, Cellerino A (2006) Shape analysis of female facial attractiveness. Vis Res 46:1282–1291
Vezzetti E, Marcolin F, Stola V (2013) 3D human face soft tissues landmarking method: an advanced approach. Comput Ind. 10.1016/j.compind.2013.04.006
Yang J, Zhang D, Frangi AF, Yang J-Y (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26:131–137
Zhang D, Zhao Q, Chen F (2011) Quantitative analysis of human facial beauty using geometric features. Pattern Recogn 44:940–950
Acknowledgments
Heartfelt thanks goes to Patrizia Mirigliani, head of “Miren International Srl” the company which organizes the national Miss Italia beauty contest, for providing the subjects for the scientific research program “Pilot study three-dimensional photogrammetric trends of contemporary facial Mediterranean attractiveness”. Warm thanks go to Dr. Carmela Savastano, President 2010 of SIDO (Italian Society of Orthodontics), for the support of the research. Special thanks go to other partners in the scientific research program: Prof. Roberto Deli, Medical Director of the Department responsible for Rehabilitation and Cosmetic Dentistry (UOC) of the Policlinico “A. Gemelli,” Catholic University of Rome. Prof. Alberto Laino, Department of Dentistry and Maxillofacial University of Naples “Federico II” and Prof. Raoul D’Alessio, Professor of esthetics at the School of Specialization in Orthodontics, Catholic University of Rome. Finally, thanks go to Maria Luisa Ostuni for her assistance in data analysis.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Galantucci, L.M., Di Gioia, E., Lavecchia, F. et al. Is principal component analysis an effective tool to predict face attractiveness? A contribution based on real 3D faces of highly selected attractive women, scanned with stereophotogrammetry. Med Biol Eng Comput 52, 475–489 (2014). https://doi.org/10.1007/s11517-014-1148-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-014-1148-8