Understanding the Impact of Aging on Attractiveness Using a Machine Learning Model of Facial Age Progression

Abstract

Background: Advances in machine learning age progression technology offer the unique opportunity to better understand the public's perception on the aging face. Objective: To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. Methods: Eight surveys were developed, each with 10 sets of photographs that were progressively aged with a machine learning model. Respondents rated attractiveness and masculinity or femininity of each photograph using a sliding scale (range: 0-100). Mean attractiveness scores were calculated and compared between men and women as well as between age groups. Results: A total of 315 respondents (51% men, 49% women) completed the survey. Accuracy of the facial age progression model was 85%. Females were considered significantly less attractive (−10.43, p < 0.01) and less feminine (−7.59, p < 0.01) per decade with the greatest drop over age 40 years. Male attractiveness and masculinity were relatively preserved until age 50 years where attractiveness scores were significantly lower (−5.45, p = 0.39). Conclusions: In this study, observers were found to perceive attractiveness at older ages differently between men and women.