Environmental and Lifestyle Factors Associated with Perceived Facial Age in Chinese Women

Mayes, Andrew E.; Murray, P. G.; Gunn, David A.; Tomlin, Cyrena C.; Catt, S D; Wen, Yi; Zhou, Li; Wang, Hong Q.; Catt, Michael; Granger, Stewart

doi:10.1371/journal.pone.0015270

Cited by 51 publications

(63 citation statements)

References 32 publications

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“…A possible explanation for this tough assessment may lie in the low social economic background and skin care habits of the recruited female volunteers, with only 25% of the panel reporting using a moisturizer, 15% using a sunscreen, 10% using facial cleanser and no one using anti-ageing products, according to a lifestyle questionnaire they answered when taking part in the study (data not shown). A previous study on Chinese women reported that subjects using facial moisturizer frequently looked 2.38 years younger versus those who did not, while subjects never using facial soap/cleanser looked 4.27 years older than those who frequently used them [24]. Additionally, pigmentary disorders are frequent in facial Indian skin [25] and could potentially be a key factor in this observed overestimation.…”

Section: Discussionmentioning

confidence: 92%

“…Interestingly, images from Caucasian women were perceived younger by the same Chinese panel, mostly after artificial reduction of wrinkles/sagging [22], thus pointing out how age perception is driven by ethnic background. Lifestyle factors having an influence on the perceived age of Chinese have been studied as well, notably the detrimental effect of sun exposure [23], but also factors associated with a younger look, among them, greater years of education, a lower number of household members, frequency of visits to the doctor, product usage or physical activity [24].…”

Section: Introductionmentioning

confidence: 99%

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What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

Merinville¹,

Messaraa²,

O'Connor³

et al. 2018

Cosmetics

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It remains important to investigate skin ageing signs across different skin types for targeted solutions. Limited data is available on Indian skin changes throughout ageing, hence three fields were investigated: skin features during the ageing process, their relationship with perceived age and self-declared skin ageing concerns. Photographs, skin topography, colour and biophysical measurements of 202 Indian female volunteers, 30-65 years old, were collected. Another panel of 693 naïve graders, 20-65 years old, estimated the age of photographs previously collected. Associations between 28 skin features and real/perceived age were assessed using linear correlation coefficients. Skin feature scores of an older perceived group were compared versus the scores of a younger perceived group, to establish skin features that lead to an older appearance. Additionally, the naïve graders were asked to rank 12 skin ageing concerns by importance. Twenty-four features correlated with real and perceived age. The ages of the volunteers were overestimated, especially those in their 30s. Skin features related to skin brightness suggested an older look for volunteers in their 30s. From the 40s onwards, wrinkles around the eye area, glabellar and corner of the mouth were also drivers for looking older. In the 50s, features such as upper lip wrinkles, hydration and roughness on the crow's feet were worse in the older perceived group, while nasolabial folds suggested an older appearance in the 60s. By having identified skin features that worsen with age and contribute to an older perceived face, this research will facilitate the creation of tailored products and communication for Indian women to look after their skin concerns throughout the ageing process.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

Merinville¹,

Messaraa²,

O'Connor³

et al. 2018

Cosmetics

View full text Add to dashboard Cite

show abstract

“…Despite competitive performance of some existing methods, recognizing and retrieving face images across aging variations remains a challenging problem. Facial aging is a complex process owing to variations in the balance, proportions, and symmetry of the face with varying age, gender, and race [1,2]. More precisely, the age, gender, and race are correlated in characterizing the facial shapes [3].…”

Section: Introductionmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts-demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations.

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“…Repeated facial expressions produce hyperfunctional facial rhytids, which progress with age, especially when associated with sun exposure, and in individuals who are heavy smokers or have poor diets (3)(4)(5)(6)(7). For some individuals, these lines are observed only with muscle contraction (dynamic rhytids), while for others, the lines are fully visible at rest (static rhytids).…”

Section: Introductionmentioning

confidence: 99%