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

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Citation: Facial Plastic Surgery & Aesthetic Medicine. 2023 Apr 12PMID: 37062756Department: MedStar Georgetown University Hospital/MedStar Washington Hospital Center | Otolaryngology ResidencyForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXED | Year: 2023ISSN:
  • 2689-3614
Name of journal: Facial plastic surgery & aesthetic medicineAbstract: 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.All authors: Chu E, Hakimi AA, Hollis T, Parsa KM, Reilly MJ, Shearer SCFiscal year: FY2023Digital Object Identifier: ORCID: Date added to catalog: 2023-06-28
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Journal Article MedStar Authors Catalog Article 37062756 Available 37062756

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.

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