Skin Color Distribution Research Articles

Exploratory study on the body distribution of skin color, pigmentation and, degree of tan in Central European Caucasian Women

It is well known that skin color varies by body site and with season. However, little quantitative data on the topography of skin color and pigmentation are available. Therefore, exploratory cutaneous colorimetric measurements in 20 in central European Caucasian women aged 20 to 60 years have been made at 18 body sites. Tri-stimulus L*a*b*-values, hue, and chroma are considered to describe skin color. Based on the “Individual Typology Angle”, the “Degree of Tan” was introduced to quantify the difference between constitutive and facultative pigmentation. Measurements were done in late winter and early summer to estimate potential changes by solar ultraviolet radiation. These measurements made evident that skin color obviously differs across the body in late winter. Even nearby body sites can be recognized as differently colored. A remaining degree of tan was found at permanent and intermittent exposed body sites. The remaining tan was not most pronounced at the permanently exposed sites but on the intermediate ones like the shoulder. In early summer, the degree of tan has most developed at the hands, arms, and instep, followed by the face. This study showed that besides basic differences between body sites in winter, accumulation, and degradation of tan also vary between body sites.Graphical

Abstract 24: Comparison Between the White Skin Color Distribution on the Brazil and Diagnoses of Nonmelanoma Skin Cancer

Abstract Purpose: According to the National Institute of Cancer (INCA), people with lighter skin tone have a higher chance of being stricken by Nonmelanoma skin cancer. Still, according to the same institute, this cancer is the most frequent tumor for both genders in Brazil. Therefore, it’s important to measure how impactful this risk factor is in Brazil. Methods: In this analysis, the data about the diagnosis of nonmelanoma skin cancer between the years of 2013 to 2021 in the different regions of Brazil was captured using the DATASUS Oncology Panel as data source, through the TABNET system. Also the data about the resident population of each region by color and race uncovered in the last census realized by the Brazilian Institute of Geography and Statistic (IBGE) in 2010 has been collected. This information has been correlated with the statistics about the tumor, using the Pearson coefficient as the method to measure the relation between these two variables. Results: The region that has the highest number of white people is the southeast region, with 44.152.518 people, followed by the south, with 21.456.204. The region with the lowest number of white habitants is the north region, with 3.686.144 individuals. The number of diagnoses of nonmelanoma skin cancer between the years of 2013-2021 follows a similar logic, with the highest number of cases in the southeast and south region (88.851 and 70.046 respectively). In this way, correlating both variables it has been found a determination coefficient (r2) of 0.943 and a p-value= 0.016, that demonstrate the significance of the relation. Conclusion: It’s evident that a direct relation between the number of nonmelanoma skin cancer diagnoses and the population of white skin per location in Brazil exists, as indicated by relevant value of the determination coefficient founded (r2 = 0.943), as well as the p-value < 0.05. So, the data show that, in Brazil, the lighter skin tone is an important risk factor associated with the tumor referred to, being this information important to subsidize future campaigns to prevent the disease. Citation Format: Arthur Minas Alberti, Lucas Kieling, Pedro Antonio Paludo Menna Barreto, Pedro Arthur Zimmer Silveira, Rafaela de Andrade, Fernando Azambuja, Luiza Seixas de Sá Beltramo, Gabriele Eckerdt Lech, Laura Tibola Marques da Silva, Maria Eduarda Corrêa Pereira. Comparison Between the White Skin Color Distribution on the Brazil and Diagnoses of Nonmelanoma Skin Cancer [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 24.

Characterization and functional analysis of pax3 in body color transition of polychromatic Midas cichlids (Amphilophus citrinellus).

As the representative genetic and economic trait of ornamental fish, skin color has a strong impact on speciation and adaptation. However, the genetic basis of skin color pigmentation, differentiation and change is still not understood. The Midas cichlid fish with three typical body color transition stages of "black-gray‑gold" is an ideal model system for investigating the formation and change of fish body color. In this study, to investigate the regulatory role of the pair box 3 (pax3) gene in the early body color fading process of Midas cichlids, the complete cDNA sequence (3513bp) of pax3 was successfully isolated from Midas cichlids (Amphilophus Citrinellus), and found to encode polypeptides of 491 amino acids. Expression patterns of the pax3 gene in tissues of Midas cichlids during different periods, including embryonic development and body color fading stages were detected by quantitative real-time PCR. The qRT-PCR analysis showed that pax3 was expressed in all tissues of adult fish, with a higher expression level in muscle and skin. The highest expression level in muscle tissue was significantly higher than that in other tissues (P<0.05). During embryonic development, the expression tendency of pax3 was first increased and then decreased. In the three typical stages of early skin color fading from black to gold, pax3 expression in skin, caudal fin and scales all showed a downward trend. The expression level in the black stage was significantly higher than that in other stages (P<0.05). Positive signal of pax3 protein was detected in the three typical skin color conversion stages, and the highest positive signal intensity was detected in the black stage, which was consistent with qRT-PCR results. After pax3 RNA interference, pax3 and the downstream genes mitf and tyr all decreased, while dct mRNA expression increased in the skin of fish. Western blotting also showed a decrease in pax3 protein concentration. Those results suggest that pax3 plays an important role in skin color formation, distribution and change in Midas cichlids through the melanogenesis pathway.

Study on A face detection method based on elliptic skin color model

Human face is a natural structural target with abundant details, and its detection results are easily affected by facial details, expressions and posture changes. In color images, the distribution of skin color is not affected by the changes of facial details, expression and posture, and the speed of skin color detection is very fast. This paper proposes a face detection algorithm based on elliptic skin color model. In YCbCr color space, the elliptic skin color model and logistic regression analysis were used to determine the skin color probability of each point, and the pixels of each point were mapped to [0, 1]. Based on Ostu method, a parallel genetic algorithm was used to determine the threshold of skin color segmentation to segment the face region. The results show that this method improves the speed of face detection and has good robustness to posture and expression changes.

Dermoscopic imaging of melasma on various skin colors

Background: Melasma is an acquired hyperpigmentation disorder characterized by pigmented macules and patches scattered on the forehead, cheeks, and chin area exposed to sunlight. Dermoscopy is a non-invasive technique that allows fast and clear observation of melasma in vivo with morphological visualization that is not visible to the naked eye. The color bar tool is an economical and simple tool that can be used to determine skin color. This study aims to determine the dermoscopic appearance of melasma on various skin colors. Methods: This study is a descriptive observational study with a cross-sectional method of all patients with melasma who came to the outpatient department of the cosmetic dermatology division, Department of Dermatology and Venereology, Universitas Sumatera Utara Hospital, Medan, Indonesia, from September 2020-October 2021. The assessment was carried out by dermoscopy and color bar tools. Results: This study included 50 melasma patients in total. Most participants (n=23, or 46 %) were between the ages of 36 and 45. Based on clinical features, 42 subjects (84%) presented with centrofacial pattern. In epidermal type melasma, 21 subjects (42%) were associated with color bar 3. In dermal type melasma, color bar 3 is found in 2 subjects (4%); in mixed type melasma, three subjects (6%) were found in color bar 3. Conclusion: The dermoscopy of melasma in various skin colors has a similar distribution of skin color. Color bar 3 is the most dominant skin color. There were no color bars 5 and color bars 6 in this study.

Real Time Skin Color Detection Based on Adaptive HSV Thresholding

The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.

Guided Facial Skin Color Correction

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

Study of the characteristics of facial skin tone status in 1092 young Chinese females according to the ITA°.

The ITA° is the gold standard for skin tone classification. Different skin tones are often associated with different skin characteristics and issues. Different skin types are often associated with different skin characteristics and issues in China. To study the population's skin color distribution and accompanying skin problems according to the ITA° classification standard. A total of 1092 women aged 22-42years were recruited in 7 cities in China. All biophysical parameter measurements (SM, CM, TWEL, pH, R2, GLOSS_DSCT, MEXA, ERYTH, ITA°; 9 indexes total) were quantified with noninvasive instruments. All volunteers provided consent before enrollment. The main skin color categories were light (II), very light (I), intermediate (III), and tan (IV). The results demonstrated that the characteristics of the facial skin based on the ITA° were significantly different among cities and age groups and were associated with different skin issues. Lighter skin was associated with worse skin elasticity; intermediate skin was associated with worse skin hydration content and was most prone to being oily; and darker skin was associated with poor barrier function. Established principal component regression (PCR) indicated that pH, gloss GLOSS_DSC, MEXA, ERYTH, TEWL, and SM had significant effects on the ITA°.

Comparisons in perception of facial skin brightness, as influenced by differences in skin color: Asian observers

AbstractHuman skin color is one of the most common colors that we see and perceive in everyday life. Previous research on skin color distribution in young Japanese women has revealed that yellowish skin has higher lightness than reddish skin. Furthermore, reddish skin appears brighter than yellowish skin when both have the same lightness (Yoshikawa et al., 2012). Despite these findings, it is unclear how brightness perception of facial skin color is influenced by the diversity of skin colors and observers. The present study investigated the perception of facial skin color among observers in three Asian countries. A young Japanese female face was used, and test faces of four skin color shades were prepared, corresponding to the average skin colors of Japanese, Thai, Caucasian, and African individuals. A test image (with constant lightness but different hue angles) and a scale image (with the original hue but varying lightness) were presented side‐by‐side on a color‐calibrated tablet display, and observers adjusted the brightness of the facial skin of the scale image to match that of the test image. The findings indicated that Japanese observers showed a trend in which reddish skin was perceived as brighter than yellowish skin; however, Thai and Chinese observers did not show this trend. This implies the potential influence of ethnicities or environments on brightness perception of facial skin.

Analysis of Skin Color on the American Academy of Dermatology Public Education Website.

Access to health information online has resulted in increased internet use to !nd medical education resources.1,2 The United States (US) population is comprised of 60.1% Whites, 18.5% Hispanics or Latinos, 13.4% Blacks or African Americans, with trends toward increasing diversity. Our objectives were to analyze skin color distributions on the American Academy of Dermatology (AAD) public education website.

Visual racism in internet searches and dermatology textbooks

Visual racism in internet searches and dermatology textbooks

Analysis of face detection based on skin color characteristic and AdaBoost algorithm

For the face detection method based on skin color feature and AdaBoost algorithm, if one of them is used to detect the face, it can also catch the face to a certain extent. However, the detection rate and an error rate of this single method in its detection experiment can’t achieve good results. Therefore, this paper combines the advantages of the two algorithms, combines the two approaches, and improves them. The main idea is to use the skin color features of face detection as pre-detection, and use the established skin color distribution Gaussian model to obtain candidate regions containing the skin color of the face, and then use a cascade classifier to detect the skin color regions. By using OpenCV and Visual Studio software, a lot of experimental statistics and analysis are carried out. The research shows that the improved algorithm is superior to the two algorithms in detection rate and false detection rate, and it can also achieve a good detection effect for the face in a complicated situation.

Cover Information

Color Research & ApplicationVolume 45, Issue 2 p. i-i COVER INFORMATIONFree Access Cover Information First published: 04 February 2020 https://doi.org/10.1002/col.22483AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The cover image is based on the Research Article Development of an image evaluation method for skin color distribution in facial images and its application: Aging effects and seasonal changes of facial color distribution by Kumiko Kikuchi et al., https://doi.org/10.1002/col.22469. Volume45, Issue2April 2020Pages i-i RelatedInformation

Development of an image evaluation method for skin color distribution in facial images and its application: Aging effects and seasonal changes of facial color distribution

AbstractIn this article, we present an evaluation method for the skin color distribution in the face area. Unlike previous methods that extract a specific area, our method subdivides the entire face into small regions and analyzes detailed, per‐frame textures. Our evaluation method for skin color distribution is based on facial feature points and includes segmentation that takes into account the facial skeleton and muscle orientation. The use of facial feature points enables a comparison at relatively equal positions on the face without depending on the shape or size of the individual's face. Our evaluation method is versatile, and as an application, we clarified age‐specific features and seasonal variations of facial color distribution. As a result of applying this evaluation method to the facial images of women aged 20 to 78 years, we confirmed that the lightness of the face decreased as age increased. In particular, the decrease in lightness was remarkable in the region along the cheekbone, from the temple to the center of the cheek. Furthermore, we analyzed the seasonal changes of melanin distribution in the face area. This showed that the melanin index increased particularly in the cheekbone area in the summer when the influence of ultraviolet rays became large. Our novel methodology and the data presented in this article will be useful in various fields, such as dermatology, cosmetics, and computer vision.

Platyrrhine color signals: New horizons to pursue.

Like catarrhines, some platyrrhines show exposed and reddish skin, raising the possibility that reddish signals have evolved convergently. This variation in skin exposure and color combined with sex‐linked polymorphic color vision in platyrrhines presents a unique, and yet underexplored, opportunity to investigate the relative importance of chromatic versus achromatic signals, the influence of color perception on signal evolution, and to understand primate communication broadly. By coding the facial skin exposure and color of 96 platyrrhines, 28 catarrhines, 7 strepsirrhines, 1 tarsiiform, and 13 nonprimates, and by simulating the ancestral character states for these traits, we provide the first analysis of the distribution and evolution of facial skin exposure and color in platyrrhini. We highlight ways in which studying the presence and use of color signals by platyrrhines and other primates will enhance our understanding of the evolution of color signals, and the forces shaping color vision.

Effective detection of exposed target regions based on deep learning from multimedia data

With the development of high-performance visual sensors, it has been very easy to obtain a variety of image data. Of these image data, human face regions contain personal information to distinguish one from the others. Therefore, it is important to accurately detect unhidden face regions from an input image. This paper proposes a method of robustly detecting human face regions from an input color image with the use of a deep learning algorithm, one of the machine learning algorithms. The proposed method first transforms the RGB color model of an input image to the YCbCr color model, and then removes other regions than face regions to segment skin regions with the use of the pre-learned elliptical skin color distribution model. Subsequently, a CNN model-based deep learning algorithm was applied to robustly detect human face regions from the detected skin regions in the previous step. As a result, the proposed method segments face regions more efficiently than an existing method. The face region detection method proposed in this paper is expected to be usefully applied to practical areas related to multimedia data processing, such as video surveillance, target blocking, image security, visual data analysis, and object recognition and tracking.

顔面における肌色分布の評価法とその応用 —加齢変化,季節変化—

顔面における肌色分布の評価法とその応用 —加齢変化,季節変化—

Visual Perception of British Women's Skin Color Distribution in Two Nonindustrialized Societies, the Maasai and the Tsimane'.

In women with lightly pigmented skin in particular, facial skin color homogeneity decreases with age, primarily due to chronic exposure to solar ultraviolet radiation (UVR), leading to a decrease in perceived health and attractiveness. Perception of female skin may be influenced by continuous exposure to, and thus familiarity with, age-related changes in visible skin condition in a given society. Men and women of two traditional societies, the Maasai (Tanzania) and the Tsimane' (Bolivia), unfamiliar with lighter colored skin, judged images of British women's facial skin for age, health, and attractiveness. In both samples, images with homogeneous skin color (from the cheeks of younger women) were judged to be younger and healthier and received a stronger preference than corresponding images with heterogeneous skin color (from older women). We suggest that (i) human sensitivity for quality-related information from facial skin color distribution is universal and independent of any known age-dependent variation in skin in a given population and (ii) skin discoloration is universally associated with less positive judgment.

Long‐term changes in Japanese women's facial skin color

AbstractIt has been suggested that skin color changes not only with advancing age but also with the times. The purpose of this study was to investigate changes in Japanese women's facial skin color over 25 years, as well as the changes in skin pigmentation that affect skin color. First, skin color changes in terms of Munsell color values were investigated. A total of 3181 Japanese women residing in the greater Tokyo area were enrolled, and datasets were collected using spectrophotometers, designated as the 1991, 2001, 2005, and 2015 data. The mean Munsell hue, value, and chroma were calculated for each measurement year. Next, the concentrations of melanin and hemoglobin were calculated from spectral data, to investigate changes in skin pigmentations. Finally, Monte Carlo simulations were performed to verify that the suggested changes in skin pigmentations brought about skin color changes. As a result, skin color significantly changed toward high lightness, low saturation, and high yellowness from the 1991 data to the 2001 data. From the 2005 to 2015 data, the skin color distribution shifted toward lower saturation and increased redness. In addition, the concentration of hemoglobin decreased significantly from the 1991 data to the 2001 data, while the melanin concentration decreased significantly from the 2005 data to the 2015 data.

Combining image and global pixel distribution model for skin colour segmentation

Skin colour detection in unconstrained and natural environment is a critical research problem. Skin regions show slightly different chrominance properties for different ambient conditions. Accuracy of detection is mostly affected either due to background colour similarity with the skin and/or poor illumination condition. To address this specific issue, a novel skin detection method is proposed by utilizing the information of pixel distribution in an image for a particular colour space. In our method, a local skin distribution model (LSDM) is derived from the image pixel distribution model using pixels from the facial region as reference. Finally, a combined skin distribution model is obtained by fusing the LSDM with the global skin colour distribution model. Subsequently, a dynamic region growing (DRG) method is proposed to allow the skin regions to grow dynamically. Our proposed DRG minimizes the overall detection error. Experimental results show that proposed skin detection method can more accurately segment out the skin-coloured regions. We obtained total detection error of 12.87% for a standard database. This improvement is due to the fact that both local and global skin colour information are used for skin segmentation.