Abstract
BACKGROUND: Understanding the differences in facial shapes in individuals from different races is relevant across several fields, from cosmetic and reconstructive medicine to anthropometric studies. OBJECTIVES: To determine whether there are features shared by the faces of an aesthetic female face database and if they correlate to their racial demographics using novel computer modeling. METHODS: The database was formed using the "top 100 most beautiful women" lists released by "For Him Magazine" for the last 15 years. Principal component analysis (PCA) of 158 parameters was carried out to check for clustering or racial correlation with these clusters. PCA is a machine-learning tool used to reduce the number of variables in a large data set, allowing for easier analysis of the data while retaining as much information as possible from the original data set. A review of the literature on craniofacial anthropometric differences across ethnicities was also undertaken to complement the computer data. RESULTS: Two thousand eight hundred and seventy aesthetic faces formed the database in the same racial proportion as 10,000 faces from the general population as a baseline. PCA clustering illustrated grouping by latent space parameters for facial dimensions but showed no correlation with racial demographics. There was a commonality of facial features within the aesthetic cohort, which differed from the general population. Fourteen papers were included in the review which contained 8142 individuals. CONCLUSIONS: Aesthetic female faces have commonalities in facial features regardless of racial demographic, and the dimensions of these features vary from the baseline population. There may even be a common human aesthetic proportion that transcends racial boundaries, but this is yet to be elucidated.