Functional analysis of keratinocytes in skin color using a human skin substitute model composed of cells derived from different skin pigmentation types

AbstractWe present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

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Face Image Segmentation Using Color Information and Saliency Map

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.77 ◽

2011 ◽

Vol 55-57 ◽

pp. 77-81

Author(s):

Hui Ming Huang ◽

He Sheng Liu ◽

Guo Ping Liu

Keyword(s):

Image Segmentation ◽

Human Skin ◽

Skin Color ◽

Saliency Map ◽

Face Image ◽

Color Information ◽

Attention Model ◽

Face Area ◽

Face Region ◽

The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

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Signatures of Positive Selection in Genes Associated with Human Skin Pigmentation as Revealed from Analyses of Single Nucleotide Polymorphisms

Annals of Human Genetics ◽

10.1111/j.1469-1809.2006.00341.x ◽

2007 ◽

Vol 71 (3) ◽

pp. 354-369 ◽

Cited By ~ 155

Author(s):

O. Lao ◽

J. M. de Gruijter ◽

K. van Duijn ◽

A. Navarro ◽

M. Kayser

Keyword(s):

Single Nucleotide Polymorphisms ◽

Positive Selection ◽

Human Skin ◽

Skin Pigmentation ◽

Nucleotide Polymorphisms ◽

Single Nucleotide

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Human Skin Detection in Color Images Using Deep Learning

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015070101 ◽

2015 ◽

Vol 5 (2) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Mohammadreza Hajiarbabi ◽

Arvin Agah

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Human Skin ◽

Skin Color ◽

Color Image ◽

Gaussian Model ◽

Color Images ◽

Skin Detection ◽

Learning Methods ◽

Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

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Photoprotection and Skin Pigmentation: Melanin-Related Molecules and Some Other New Agents Obtained from Natural Sources

Molecules ◽

10.3390/molecules25071537 ◽

2020 ◽

Vol 25 (7) ◽

pp. 1537 ◽

Cited By ~ 5

Author(s):

Francisco Solano

Keyword(s):

Human Skin ◽

Uv Light ◽

Stratospheric Ozone ◽

Skin Pigmentation ◽

Antioxidant Properties ◽

Sun Exposure ◽

Marine Organisms ◽

Electromagnetic Spectrum ◽

Chemical Structures ◽

Skin Lightening

Direct sun exposure is one of the most aggressive factors for human skin. Sun radiation contains a range of the electromagnetic spectrum including UV light. In addition to the stratospheric ozone layer filtering the most harmful UVC, human skin contains a photoprotective pigment called melanin to protect from UVB, UVA, and blue visible light. This pigment is a redox UV-absorbing agent and functions as a shield to prevent direct UV action on the DNA of epidermal cells. In addition, melanin indirectly scavenges reactive oxygenated species (ROS) formed during the UV-inducing oxidative stress on the skin. The amounts of melanin in the skin depend on the phototype. In most phenotypes, endogenous melanin is not enough for full protection, especially in the summertime. Thus, photoprotective molecules should be added to commercial sunscreens. These molecules should show UV-absorbing capacity to complement the intrinsic photoprotection of the cutaneous natural pigment. This review deals with (a) the use of exogenous melanin or melanin-related compounds to mimic endogenous melanin and (b) the use of a number of natural compounds from plants and marine organisms that can act as UV filters and ROS scavengers. These agents have antioxidant properties, but this feature usually is associated to skin-lightening action. In contrast, good photoprotectors would be able to enhance natural cutaneous pigmentation. This review examines flavonoids, one of the main groups of these agents, as well as new promising compounds with other chemical structures recently obtained from marine organisms.

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Characterization of Chicken Skin Yellowness and Exploration of Genes Involved in Skin Yellowness Deposition in Chicken

Frontiers in Physiology ◽

10.3389/fphys.2021.585089 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jingwen Wu ◽

Zetong Lin ◽

Genghua Chen ◽

Qingbin Luo ◽

Qinghua Nie ◽

...

Keyword(s):

Skin Color ◽

Skin Pigmentation ◽

Differentially Expressed ◽

Economic Trait ◽

Chicken Skin ◽

Before And After ◽

Skin Coloration ◽

Insight Into ◽

Selection Of

Skin color is an important economic trait in meat-type chickens. A uniform bright skin color can increase the sales value of chicken. Chickens with bright yellow skin are more popular in China, especially in the broiler market of South China. However, the skin color of chickens can vary because of differences in breeds, diet, health, and individual genetics. To obtain greater insight into the genetic factors associated with the process of skin pigmentation in chickens, we used a colorimeter and high-resolution skin photographs to measure and analyze the skin color of chickens. By analyzing 534 chickens of the same breed, age, and feed condition, we found that the yellowness values of the chickens varied within this population. A significant positive correlation was found between the cloacal skin yellowness values before and after slaughter, and the cloacal skin yellowness value of live chickens was positively correlated with the overall body skin yellowness value. Additionally, chicken skin yellowness exhibited low heritability, ranging from 0.07 to 0.27. Through RNA sequencing, 882 genes were found to be differentially expressed between the skin with the highest and lowest yellowness values. Some of these differentially expressed genes may play an important role in yellow pigment deposition in chicken skin, which included TLR2B, IYD, SMOC1, ALDH1A3, CYP11A1, FHL2, TECRL, ACACB, TYR, PMEL, and GPR143. In addition, we found that the expression and variations of the BCO2 gene, which is referred to as the yellow skin gene, cannot be used to estimate the skin yellowness value of chickens in this population. These data will help to further our understanding of chicken skin yellowness and might contribute to the selection of specific chicken strains with consistent skin coloration.

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Prediction of Eye, Hair and Skin Color in Admixed Populations of Latin America

10.1101/2020.12.09.415901 ◽

2020 ◽

Author(s):

Sagnik Palmal ◽

Kaustubh Adhikari ◽

Javier Mendoza-Revilla ◽

Macarena Fuentes-Guajardo ◽

Caio C. Silva de Cerqueira ◽

...

Keyword(s):

Native American ◽

Skin Color ◽

Prediction Accuracy ◽

Prediction Models ◽

Skin Pigmentation ◽

Genetic Ancestry ◽

Learning Approaches ◽

Latin Americans ◽

Eye Color ◽

The Impact

AbstractWe report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

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African Skin: Different Types, Needs and Diseases

International Journal of TROPICAL DISEASE & Health ◽

10.9734/ijtdh/2019/v36i330143 ◽

2019 ◽

pp. 1-13

Author(s):

Olumayowa Abimbola Oninla ◽

Samuel Olorunyomi Oninla ◽

Bolaji Ibiesa Otike-Odibi ◽

Mufutau Muphy Oripelaye ◽

Fatai Olatunde Olanrewaju ◽

...

Keyword(s):

Skin Color ◽

Skin Diseases ◽

Skin Pigmentation ◽

Geographical Location ◽

Skin Care ◽

Skin Type ◽

Protective Properties ◽

Tropical Regions ◽

The Right

Microscopic structures in the skin are basically the same in all races. Differences are found in histology and physiology of the skin resulting in different skin types, needs and prevailing skin diseases. Skin pigmentation (with the photo-protective properties), and the barrier function of the stratum corneum are the main differences between African and Caucasian skin. The geographic distribution of UV radiation (UVR) has a positive correlation with geographical location. The darker-skinned populations are closer to the equator where there are high amounts of UVR especially in the tropical regions of the world. African skin has the greatest variability in skin color. Africa has both white and dark skinned individuals with the darker-skinned populations being mostly around the equator. Leslie Baumann introduced four parameters that more accurately characterized skin types than previous classification of dry, oily, normal and combination skin. These are dry or oily – D/O; sensitive or resistant – S/R; pigmented or non-pigmented – P/N, and wrinkled or unwrinkled skin – W/T. Combinations of these further produced sixteen skin phenotypes. Dark skinned individuals often have the PT types while the light skinned mostly have the NW types. Skin needs basically depends on the type. Identifying the skin type is fundamental to providing the right skin care. According to Baumann, the fundamental elements of skin care are mild cleansing, hydrating (moisturization with humectants and emollients), replenishing (with lipids, ceramides and fatty acids) and skin protection (UV protection and increased humidity). Skin diseases are associated with skin type. Eczema is more typical in people with DS combinations while acne is associated with OS skin type (especially OSNT and OSPT). Prevalence of skin diseases varies within African communities from 35% to 87% with skin infections affecting 22-46% and eczemas 13-21% of patients in various studies.

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