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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

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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.

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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.

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Authors and Affiliations

  1. Laboratorio di Prototipazione Rapida e Reverse Engineering, Dipartimento di Meccanica Matematica e Management, Politecnico di Bari, viale Japigia 182, 70126, Bari, Italy

    Luigi Maria Galantucci & Gianluca Percoco

  2. Studio Associato di Odontoiatria dei Dottori Di Gioia, Via Dante Alighieri 97, 70122, Bari, Italy

    Eliana Di Gioia

  3. Polishape 3D srl, viale Japigia 182, 70126, Bari, Italy

    Fulvio Lavecchia

Authors
  1. Luigi Maria Galantucci
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  2. Eliana Di Gioia
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  3. Fulvio Lavecchia
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  4. Gianluca Percoco
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Corresponding author

Correspondence to Luigi Maria Galantucci.

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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

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