The automatic measurement of facial beauty

We develop an automatic facial beauty scoring system based on ratios between facial features. After isolating the face, eyes, eyebrows and mouth in a portrait photograph, we represent a face abstractly as an 8-element vector of ratios between these features. We use a variant of the K-nearest neighbor algorithm, in the context of a parameterized metric space optimized using a genetic algorithm, to learn a beauty assignment function from a training set of photographs rated by humans. We assess performance on a test set of photographs, concluding that when facial ratios are accurately extracted in the computer vision phase, the results of the program are highly correlated with median-human ratings of beauty.