Perceived Age and Gender Perception Using Facial Recognition Software Following Facial Feminization Surgery

Abstract

Measures of success for facial feminization surgery (FFS) have previously included improved rates of external gender perception as female and patient-reported outcome measures. In this study, we used artificial intelligence facial recognition software to objectively evaluate the effects of FFS on both perceived gender and age among male-to-female transgender patients, as well as their relationship with patient facial satisfaction. Standardized frontal preoperative and postoperative images of 27 transgender women undergoing FFS were analyzed by Amazon’s AI facial recognition software to determine gender, femininity confidence score, and perceived age. Female gender-typing, improvement in gender-typing (preoperatively to postoperatively), and femininity confidence scores were analyzed. To assess patient satisfaction, FACE-Q modules were completed postoperatively. Preoperatively, FFS images were perceived as female 48.1% of the time, and postoperatively, this improved to 74.1% (P=0.05). Femininity confidence scores improved from a mean score of 0.04 preoperatively to 0.39 postoperatively (P=0.003). FFS was associated with a decrease in perceived age relative to the patient’s true age (−2.4 y, P<0.001), with older patients experiencing greater reductions. Pearson correlation matrix found no significant relationship between improved female gender typing and patient facial satisfaction. Undergoing surgery at a younger age was associated with higher overall facial satisfaction (r=−0.6, P=0.01). Transfeminine patients experienced improvements in satisfaction with facial appearance, perceived gender, and decreases in perceived age following FFS. Notably, patient satisfaction was not directly associated with improved AI-gender typing, suggesting that other factors may influence patient satisfaction.