Galantucci Luigi Maria, Di Gioia Eliana, Lavecchia Fulvio, Percoco Gianluca
Laboratorio di Prototipazione Rapida e Reverse Engineering, Dipartimento di Meccanica Matematica e Management, Politecnico di Bari, viale Japigia 182, 70126, Bari, Italy,
Med Biol Eng Comput. 2014 May;52(5):475-89. doi: 10.1007/s11517-014-1148-8. Epub 2014 Apr 12.
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.
在文献中，有几篇论文报道了关于用于描述面部特征以及基于三维人脸数据预测女性面部美的数学模型的研究。许多作者提出了主成分分析（PCA）方法，该方法允许使用有限数量的参数对面部整体进行建模。在某些情况下，这些模型已与美貌分类相关联，通过包裹的二维或三维模型获得了良好的吸引力预测能力。为了验证这些结果，在本文中，作者使用一种名为三维数字摄影测量的计算机化非侵入性工具，对66名极具吸引力的女性受试者进行了三维数字化研究。样本包括2010年意大利小姐选美比赛决赛阶段的64名参赛者，以及2009年比赛中排名最高的两名参赛者。对这个真实面部样本进行了主成分分析，以验证排名与面部模型的主成分之间是否存在相关性。结果不存在相关性，因此，对于我们的样本，这一假设未得到证实。考虑到比赛结果不仅完全取决于面部吸引力，而且无疑会受到其显著影响，作者基于他们的经验和真实面部得出结论，主成分分析不是一种有效的吸引力预测工具。分析样本的特征数据库可在线下载，欢迎进一步提供资料。
