Developing an Artificial Intelligence (A.I)-based descriptor of facial appearance that fits with the assessments of makeup experts.

OBJECTIVE: To develop an A.I-based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making-up procedures. MATERIAL AND METHODS: The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists. Each selfie image was annotated by 12 experts and defined references to train Artificial Intelligence (A.I)-based algorithm. RESULTS: As some the 23 signs present a continuous or discontinuous feature, these were analyzed by two different statistical approaches. The results provided by the automatic descriptor system were not only in good agreement with the expert's assessments but were even found of a better precision and reproducibility. This automatic descriptor system has proven a good and robust accuracy despite the very variable conditions in the acquisition of selfie pictures. CONCLUSION: Such automatic descriptor system seems providing a valuable help in making-up procedures and may extend to other activities such as Skincare or Haircare. As such it should allow large investigations to better evaluate the consumers' needs of esthetical improvements.

Japanese experiment of a complete and objective automatic grading system of facial signs from selfie pictures: Validation with dermatologists and characterization of changes due to age and sun exposures.

OBJECTIVE: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of ten facial signs in Japanese women and their changes due to age and sun exposures. METHODS: A three-step approach was conducted, based on self-taken selfie images. At first, to check on 310 Japanese women (18-69 years) enrolled in the northerner Hokkaido area (latitude 43.2°N), how, on ten facial signs, the A.I-based automatic grading system may correlate with dermatological assessments, taken as reference. Second, to assess and compare age changes in 310 Japanese and 112 Korean women. Third, as these Japanese panelists were recruited according to their usual behavior toward sun exposure, that is, non-sun-phobic (NSP, N = 114) and sun-phobic (SP, N = 196), and through their regular and early use of a photo-protective product, to characterize the facial photo-damages. RESULTS: (a) On the ten facial signs, detected automatically, nine were found significantly (P < .0001) highly correlated with the evaluations made by three Japanese dermatologists (Wrinkles: r = .75; Sagging: r = .80; Pigmentation: r = .75). (b) The automatic scores showed significant changes with age, by decade, of Wrinkles/Texture, Pigmentation, and Ptosis/Sagging (P < .05). (c) After 45 years, a significantly increased severity of Wrinkles/Texture and Pigmentation was observed in NSP vs. SP women (P < .05). A trend of an increased Ptosis/Sagging (P = .09) was observed. CONCLUSION: This work illustrates, for the first time through investigations conducted at home, some impacts of aging and sun exposures on facial signs of Japanese women. Results significantly confirm the importance of sun avoidance coupled with photo-protective measures. In epidemiological studies, the AI-based system offers a fast, affordable, and confidential approach in detection and quantification of facial signs and their dependence with ages, environments and lifestyles.