New method for large-scale facial skin sebum quantification and skin type classification

Women's skin care behaviors: How to influence sunscreen use

BACKGROUND

It is well known that women have been plagued by various skin problems. However, research on the characteristics of women's skin at different ages is still inadequate. In addition, there is a lack of research on the extent of women's skincare habits and skin care awareness.

METHODS

A cross-sectional survey on skin was carried out in Shanghai, China, which was conducted by means of a questionnaire. 3678 women, aged 18-59 years, participated in the study. The information collected focused on the importance they place on their skin, the skin problems they have, and their use and perception of skin care products.

RESULTS

Before the age of 25, the most common skin problems that women face are dryness and oiliness, while after the age of 30, skin-ageing issues begin to appear and worsen with age. In addition, the higher the level of education, the higher the frequency of and compliance with sunscreen use, and the economy also affects women's use of sunscreen. Importantly, the importance women place on their skin and the level of sunscreen awareness affects women's use of sunscreen.

CONCLUSIONS

This study was conducted to understand the skin characteristics of women of different age groups as well as to determine the factors that influence the use of sunscreens, which will not only promote women's skin care practices and product development, but also provide important clues for future activities on sunscreen use and health promotion.

An optimal method for quantifying the facial sebum level and characterizing facial sebum features

BACKGROUND

Evidence suggests that sebum content is important in skin disorders such as acne. However, sebum levels change depending on the external environment, and quantifying skin sebum levels is challenging. Here, we propose an optimal method for quantifying the facial sebum level.

MATERIALS AND METHODS

Four hundred and sixty participants (160 males and 300 females) aged 20-40 were enrolled in this study. A Sebumeter SM 810 was used to measure the sebum level at five facial locations: the forehead, the chin, the left cheek, the right cheek, and the nose. The participants were divided into two groups; one group underwent a one-time measurement (n = 390, male: female = 120: 270), and the other underwent three consecutive measurements (n = 70, male: female = 40: 30). The casual sebum level (CSL) was measured in all patients after a 30-min acclimatization; subsequently, the sebum removal process was conducted, followed by a resting period of 1 h to determine the sebum excretion rate (SER). Spearman's correlation analysis and the Wilcoxon signed-rank test were used to compare the sebum level consistency and differences between the groups.

RESULTS

Although three consecutive measurements better reflected the sebum content, the one-time measurement also represented the relative sebum level. One hour after sebum removal, the sebum level recovered to 70%-90%; thus, this method was applicable for use in SER quantification. Of the five testing points, the sebum content was highest in the nose and lowest in the cheeks (both left and right). In addition, the cheeks were the most stable sites in terms of testing points, testing times, and CSL/SER values. A one-time measurement of the CSL could represent the SER 1 h after the sebum removal. In our cohort, the sebum level of males with oily skin was decreased at age 32-35, and that of males with non-oily skin increased at 28-35. The opposite trend was observed in female participants.

CONCLUSION

Sebum measurement methods were assessed, including testing times, indices (interval of time) and sites in a conditioned external environment. A one-time measurement of the CSL 1 h after sebum removal was sufficient to determine the sebum level and SER, and the cheeks are recommended as the testing site. Sex and skin type differences were observed in sebum level changes with age.

An Optimization-Based Technology Applied for Face Skin Symptom Detection

Face recognition segmentation is very important for symptom detection, especially in the case of complex image backgrounds or noise. The complexity of the photo background, the clarity of the facial expressions, or the interference of other people's faces can increase the difficulty of detection. Therefore, in this paper, we have proposed a method to combine mask region-based convolutional neural networks (Mask R-CNN) with you only look once version 4 (YOLOv4) to identify facial symptoms by this new method. We use the face image dataset from the public image databases DermNet and Freepic as the training source for the model. Face segmentation was first applied with Mask R-CNN. Then the images were imported into ResNet-101, and the facial features were fused with region of interest (RoI) in the feature pyramid networks (FPN) structures. After removing the non-face features and noise, the face region has been accurately obtained. Next, the recognized face area and RoI data were used to identify facial symptoms (acne, freckle, and wrinkles) with YOLOv4. Finally, we use Mask R-CNN, and you only look once version 3 (YOLOv3) and YOLOv4 are matched to perform the performance analysis. Although, the facial images with symptoms are relatively few. We still use a limited amount of data to train the model. The experimental results show that our proposed method still achieves 57.73%, 60.38%, and 59.75% of mean average precision (mAP) for different amounts of data. Compared with other methods, the mAP was more than about 3%. Consequently, using the method proposed in this paper, facial symptoms can be effectively and accurately identified.

A cross-sectional study of Chinese women facial skin status with environmental factors and individual lifestyles

Geographical, environmental and pollution conditions affect facial skin health, but their effects on skin appearance have not been elucidated. This study aimed to describe the skin barrier and skin tone characteristics of Chinese subjects according to lifestyle and environmental conditions using in vitro measurements. In total, 1092 women aged 22-42 years were recruited from 7 representative Chinese cities. Eight skin parameters (hydration, sebum, pH, transdermal water loss, individual type angle, melanin index, erythema index, yellowness) were measured using noninvasive instruments; individual lifestyle data were also collected. Data on four meteorological factors (air temperature, relative humidity, sunshine duration, wind speed) and seven air pollution indicators (air quality index, fine particulate matter, breathable particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone) were collected in each city from the China Meteorological Administration. Facial skin characteristics differed significantly between cities. Facial skin barrier characteristics and skin tones showed regional differences, with a better skin barrier associated with the western region, as indicated by high skin hydration and sebum secretion and a low pH value. According to the value of transdermal water loss, lighter and darker skin tones were found in the western and southern regions, respectively. Environmental conditions affected facial skin status. Air pollution induced facial skin issues, with fine particulate matter and nitrogen dioxide contributing the most. Individual lifestyles affected the facial skin barrier and skin tone.

Facial Skin Aging Stages in Chinese Females

Background

Facial skin is exposed to the environment, which marks it with obvious signs of aging. Based on multi-dimensional non-invasive evaluation data, female facial skin can be characterized in detail. However, there are few studies on the general aging rules of facial skin. Most skin aging studies divide the ages into 5-10-year intervals, so they have lacked dynamic matching with facial skin aging.

Aim

To explore facial skin aging rules, discuss the main parameters of facial skin aging, propose an unequal-distance aging division method based on the main skin parameters, and study the skin characteristics of Chinese women of different aging stages.

Methods

We comprehensively described the skin status as 24 non-invasive skin parameters belonging to five dimensions: skin wrinkles, texture, stain, color and barrier function. We performed polynomial fitting on the 21 skin parameters that were significantly correlated with age and derived the rules of aging in the different dimensions. Based on the wrinkle dimension, the facial skin aging process was divided into four stages, and the skin characteristics of the different stages were compared.

Results

Skin wrinkles increased, texture deteriorated, acne decreased, pigment spots increased, skin tone darkened, and sebum secretion decreased with age, according to the polynomial fitting. The aging stage was divided into an incubation period (18-30 years old), an aging occurrence period (31-42 years old), a rapid aging period (43-47 years old), and a stable aging period (48-60 years old), according to the wrinkles. Different aging stages had different skin characteristics.

Conclusion

The incubation period is the critical period for the appearance of skin stains; the skin texture gradually deteriorates during the aging occurrence period; the rapid aging period is a critical period for the aging of skin parameters; skin status during the stable aging period is the worst.

An objective skin-type classification based on non-invasive biophysical parameters

BACKGROUND

Despite the invention of various non-invasive bioengineering tools, skin-type analysis has largely been based on subjective assessments. However, advancements in the functional cosmetic industry and artificial intelligence-assisted dermatology are creating a greater demand for an objective skin-type classification system.

OBJECTIVES

To propose an objective skin-type classification system solely based on non-invasive, bioengineering devices; provide reference values applicable to the Korean population; and compare our reference values with those of published studies.

METHODS

Biophysical parameter measurements were obtained from the 2018 International Skin Characteristics Data Bank Project conducted by the Foundation of Korea Cosmetic Industry Institute. The participants were 434 healthy South Korean adults. Each participant was assessed using eight bioengineering devices (Tewameter^® , pH-meter^® , Corneometer^® , Sebumeter^® , Cutometer^® , Spectrophotometer^® , PRIMOS^® lite, and Janus^® ). The measurements were divided into tertiles to determine reference points.

RESULTS

Our objective skin-type classification consists of five main categories (sensitivity, hydration, oiliness, elasticity, and skin tone) and five corresponding subcategories (erythema, roughness, pores, wrinkles, and pigmentation, respectively). Each skin type was assigned based on the reference point of the biophysical parameter, which was established as the tertile value associated with 'unfavourable' skin characteristics. Individuals were categorized as having sensitive skin when the TEWL scores were over 18.0 g/m² /h or the pH was over 5.45; dehydrated skin when the corneometric value measured below 47.17 A.U.; oily skin when the sebumetric value exceeded 70 μg/cm² ; and loose skin when the cutometric R² value was below 0.68 E/mm.

CONCLUSIONS

This study is the first to provide a comprehensive skin-type classification system based solely on non-invasive biophysical parameters. As measurement data accumulate, the reference points will progress to become more accurate, and they will be subdivided according to gender, age, and ethnic group. Therefore, our classification system serves as a basis for artificial intelligence-based skin-type analysis.