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Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies

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  • Yi Feng Wen
  • Hai Ming Wong
  • Ruitao Lin
  • Guosheng Yin
  • Colman McGrath

Abstract

Background: Numerous facial photogrammetric studies have been published around the world. We aimed to critically review these studies so as to establish population norms for various angular and linear facial measurements; and to determine inter-ethnic/racial facial variations. Methods and Findings: A comprehensive and systematic search of PubMed, ISI Web of Science, Embase, and Scopus was conducted to identify facial photogrammetric studies published before December, 2014. Subjects of eligible studies were either Africans, Asians or Caucasians. A Bayesian hierarchical random effects model was developed to estimate posterior means and 95% credible intervals (CrI) for each measurement by ethnicity/race. Linear contrasts were constructed to explore inter-ethnic/racial facial variations. We identified 38 eligible studies reporting 11 angular and 18 linear facial measurements. Risk of bias of the studies ranged from 0.06 to 0.66. At the significance level of 0.05, African males were found to have smaller nasofrontal angle (posterior mean difference: 8.1°, 95% CrI: 2.2°–13.5°) compared to Caucasian males and larger nasofacial angle (7.4°, 0.1°–13.2°) compared to Asian males. Nasolabial angle was more obtuse in Caucasian females than in African (17.4°, 0.2°–35.3°) and Asian (9.1°, 0.4°–17.3°) females. Additional inter-ethnic/racial variations were revealed when the level of statistical significance was set at 0.10. Conclusions: A comprehensive database for angular and linear facial measurements was established from existing studies using the statistical model and inter-ethnic/racial variations of facial features were observed. The results have implications for clinical practice and highlight the need and value for high quality photogrammetric studies.

Suggested Citation

  • Yi Feng Wen & Hai Ming Wong & Ruitao Lin & Guosheng Yin & Colman McGrath, 2015. "Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-20, August.
    • Handle: RePEc:plo:pone00:0134525
    DOI: 10.1371/journal.pone.0134525
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    1. Adrian Sergiu Darabant & Diana Borza & Radu Danescu, 2021. "Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study," Mathematics, MDPI, vol. 9(2), pages 1-19, January.

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