Advances in machine learning age progression technology offer the unique opportunity to better understand the public’s perception on the aging face. To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. 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. 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, < 0.01) and less feminine (-7.59, < 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, = 0.39). In this study, observers were found to perceive attractiveness at older ages differently between men and women.
