Objective and automatic grading system of facial signs from selfie pictures of South African women: Characterization of changes with age and sun-exposures.

Objective: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of fifteen facial signs in South African women and their changes related to age and sun-exposure habits.

Methods: A two-steps approach was conducted based on self-taken selfie images. At first, to assess on 306 South African women (20-69 years) enrolled in Pretoria area (25.74°S, 28.22°E), age changes on fifteen facial signs measured by an artificial intelligence (AI)-based automatic grading system previously validated by experts/dermatologists. Second, as these South African panelists were recruited according to their usual behavior toward sun-exposure, that is, nonsun-phobic (NSP, N = 151) and sun-phobic (SP, N = 155) and through their regular and early use of a photo-protective product, to characterize the facial photo-damages.

Results: (1) The automatic scores showed significant changes with age, by decade, of sagging and wrinkles/texture (p < 0.05) after 20 and 30 years, respectively. Pigmentation cluster scores presented no significant changes with age whereas cheek skin pores enlarged at a low extent with two plateaus at thirties and fifties. (2) After 60 years, a significantly increased severity of wrinkles/texture and sagging was observed in NSP versus SP women (p < 0.05). A trend of an increased pigmentation of the eye contour (p = 0.06) was observed after 50 years.

Conclusion: This work illustrates specific impacts of aging and sun-exposures on facial signs of South African women, when compared to previous experiments conducted in Europe or East Asia. Results significantly confirm the importance of sun-avoidance coupled with photo-protective measures to avoid long-term skin damages. In inclusive epidemiological studies that aim at investigating large human panels in very different contexts, the AI-based system offers a fast, affordable and confidential approach in the detection and quantification of facial signs and their dependency with ages, environments, and lifestyles.