The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population.

BACKGROUND: The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity. OBJECTIVE: To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women. METHODS: In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool. RESULTS: For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted. CONCLUSION: In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.

Accuracy and clinical relevance of an automated, algorithm-based analysis of facial signs from selfie images of women in the United States of various ages, ancestries and phototypes: A cross-sectional observational study.

BACKGROUND: Real-life validation is necessary to ensure our artificial intelligence (AI) skin diagnostic tool is inclusive across a diverse and representative US population of various ages, ancestries and skin phototypes. OBJECTIVES: To explore the relevance and accuracy of an automated, algorithm-based analysis of facial signs in representative women of different ancestries, ages and phototypes, living in the same country. METHODS: In a cross-sectional study of selfie images of 1041 US women, algorithm-based analyses of seven facial signs were automatically graded by an AI-based algorithm and by 50 US dermatologists of various profiles (age, gender, ancestry, geographical location). For automated analysis and dermatologist assessment, the same referential skin atlas was used to standardize the grading scales. The average values and their variability were compared with respect to age, ancestry and phototype. RESULTS: For five signs, the grading obtained by the automated system were strongly correlated with dermatologists' assessments (r ≥ 0.75); cheek skin pores were moderately correlated (r = 0.63) and pigmentation signs, especially for the darkest skin tones, were weakly correlated (r = 0.40) to the dermatologist assessments. Age and ancestry had no effect on the correlations. In many cases, the automated system performed better than the dermatologist-assessed clinical grading due to 0.3-0.5 grading unit differences among the dermatologist panel that were not related to any individual characteristic (e.g. gender, age, ancestry, location). The use of phototypes, as discontinuous categorical variables, is likely a limiting factor in the assessments of grading, whether obtained by automated analysis or clinical assessment of the images. CONCLUSIONS: The AI-based automatic procedure is accurate and clinically relevant for analysing facial signs in a diverse and inclusive population of US women, as confirmed by a diverse panel of dermatologists, although skin tone requires further improvement.

A 5-hour follow-up of the impact on ageing facial signs of some foundations in Japanese women through automatically analysed selfie pictures.

OBJECTIVE: To explore the possibility of an automatic grading algorithm to detect and quantify, from selfie pictures, the subtle changes of facial signs brought by the application of a foundation. MATERIAL AND METHODS: A total of 270 Japanese differently aged women (30-54y) living in four different Japanese cities took selfies before, immediately and 5 h after having applied their own foundation, with their own routine. RESULTS: The analysis of 810 selfie pictures revealed (3 times × 270 women) that, prior to applications, all women presented a low grade of facial ageing. In most cases, the severities of less marked facial signs were detected and quantified, found affected by routine at different extents in all age-classes, but more intensely in the older age-class (45-54y) despite their more pronounced signs in bare skin status. In contrast, periorbital wrinkles were detected as more severe in all age-classes at both timing, that is, immediately and 5 h post-application as well as Nasolabial folds 5 h post-application. The amplitude of these positive or negative changes, although found of low amplitude, as decimals of the initial grades, was significantly detected. CONCLUSION: This automatic system appears apt at grading subtle changes in facial ageing signs brought by a foundation and could be a valuable help to the consumers of make-up products, in refining their individual procedure to obtain a more personalized desired facial appearance.

OBJECTIF: Explorer la possibilité pour un algorithme de scorage automatique des signes faciaux de détecter et quantifier sur la base de photographies 'selfies', les changements subtils apportés par l'application et la tenue de fonds de teint. MATÉRIEL ET MÉTHODES: 270 femmes japonaises âgées de 30 à 54 ans et vivant dans 4 villes différentes ont pris des selfies sur la base de leur smartphone avant, immédiatement et 5 heures après avoir appliqué leurs routines cosmétiques incluant leur propre fond de teint. RÉSULTATS: L'analyse des 810 selfies (3 temps×270 femmes) a révélé que, avant application, la plupart des femmes présentent des grades faibles de vieillissement facial. Dans la plupart des cas, les sévérités des signes les moins marqués, détectés et quantifiés, a été trouvée affectée à différents niveaux dans toutes les classes d'âge, mais plus intensément pour les volontaires les plus âgées (45 à 54 ans) en dépit de leurs valeurs plus élevées sur peau nue. A contrario, les rides péri-orbitales ont été mesurées comme plus sévères dans toutes les classes d'âge à tous les temps, c'est-à-dire immédiatement et 5 heures apprès application du fond de teint tandis que le sillon nasogénien a été observé comme plus sévère 5 heures après application. L'amplitude de ces changements positifs ou négatifs, bien que faible avec des valeurs décimales des grades initiaux, a été significativement détectée. CONCLUSIONS: Le système de scorage automatique apparaît capable d'évaluer des changements subtils dans les signes de vieillissement faciaux apportés par l'application de fonds de teint et se révèle une aide intéressante aux consommateurs de produits de maquillage pour affiner leurs routines individuelles afin d'obtenir des résultats plus personnalisés sur l'apparence désirée.

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.

The continuous development of a complete and objective automatic grading system of facial signs from selfie pictures: Asian validation study and application to women of three ethnic origins, differently aged.

OBJECTIVE: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of seven new facial signs added to the nine previously integrated. METHODS: A two-step approach was conducted: first, to check on 112 Korean women, how the AI-based automatic grading system may correlate with dermatological assessments, taken as reference; second, to confirm on 1140 women of three ancestries (African, Asian, and Caucasian) the relevance of the newly input facial signs. RESULTS: The sixteen specific Asian facial signs, detected automatically, were found significantly (P < .0001) highly correlated with the clinical evaluations made by two Korean dermatologists (wrinkles: r = .90; sagging: r = .75-.95; vascular: r = .85; pores: r = .60; pigmentation: r = .50-.80). When applied at a larger scale on women of different ethnicities, new signs were found of good accuracy and reproducibility, albeit depending on ethnicity. Due to contrast with the innate skin complexion, the facial signs dealing with skin pigmentation were found of a much higher relevance among Asian women than African or Caucasian women. The automatic gradings were even found of a slightly higher accuracy than the clinical gradings. CONCLUSION: The previously used automatic grading system is now completed by adding new facial signs apt at being detected. The continuous development is now integrating some limitations with regard to the constitutive skin complexion of the self-pictured subjects. Presenting reproducible assessments, highly correlated with medical grading, this system could change tremendously clinical researches, like in epidemiological studies, where it offers an easy, fast, affordable, and confidential approach in the objective quantification of facial signs.