Journal articles: 'Human skin color'

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Kirjava, Shade Avery, and Sam Jones Faulkner. "Over-the-Counter (OTC) Hearing Aid Availability across the Spectrum of Human Skin Colors." Audiology Research 14, no. 2 (March 12, 2024): 293–303. http://dx.doi.org/10.3390/audiolres14020026.

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Background: Over-the-counter (OTC) hearing aids were recently approved for sale in the United States. Research has shown that consumers prefer hearing devices that match their skin color because these devices are less noticeable. Colorism is discrimination against individuals with relatively darker skin that manifests in “skin-color” product offerings as products being offered primarily in relatively lighter colors. Methods: This study compared images of U.S. Food and Drug Administration (FDA)-registered over-the-counter hearing aids to a range of human skin colors. Results: Most over-the-counter hearing aids are only offered in relatively lighter beige colors. Few over-the-counter hearing aids are available in darker skin colors. Conclusions: These findings may represent structural bias, preventing equitable access to darker skin-color OTC hearing aids for individuals with darker skin.

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Human Skin Detection in Color Images Using Deep Learning." International Journal of Computer Vision and Image Processing 5, no. 2 (July 2015): 1–13. http://dx.doi.org/10.4018/ijcvip.2015070101.

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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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Hajiarbabi, Mohammadreza, and Arvin Agah. "Human Skin Color Detection Using Neural Networks." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 425–36. http://dx.doi.org/10.1515/jisys-2014-0098.

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AbstractHuman skin detection is an essential phase in face detection and face recognition when using color images. Skin detection is very challenging because of the differences in illumination, differences in photos taken using an assortment of cameras with their own characteristics, range of skin colors due to different ethnicities, and other variations. Numerous methods have been used for human skin color detection, including the Gaussian model, rule-based methods, and artificial neural networks. In this article, we introduce a novel technique of using the neural network to enhance the capabilities of skin detection. Several different entities were used as inputs of a neural network, and the pros and cons of different color spaces are discussed. Also, a vector was used as the input to the neural network that contains information from three different color spaces. The comparison of the proposed technique with existing methods in this domain illustrates the effectiveness and accuracy of the proposed approach. Tests were done on two databases, and the results show that the neural network has better precision and accuracy rate, as well as comparable recall and specificity, compared with other methods.

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Yan, Yuchun, and Hyeon-Jeong Suk. "Skin Balancing: Skin Color-Based Calibration for Portrait Images to Enhance the Affective Quality." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 91–94. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.17.

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Because our sensitivity to human skin color leads to a precise chromatic adjustment, skin color has been considered a calibration target to enhance the quality of images that contain human faces. In this paper, we investigated the perceived quality of portrait images depending on how the target skin color is defined: measured, memory, digital, or CCT skin color variations. A user study was conducted; 24 participants assessed the quality of white-balanced portraits on five criteria: reality, naturalness, appropriateness, preference, and emotional enhancement. The results showed that the calibration using measured skin color best served the aspects of reality and naturalness. With regard to appropriateness and preference, digital skin color obtained the highest score. Also, the memory skin color was appropriate to calibrate portraits with emotional enhancement. In addition, the other two CCT target colors enhanced the affective quality of portrait images, but the effect was quite marginal. In the foregoing, labelled Skin Balance, this study proposes a set of alternative targets for skin color, a simple but efficient way of reproducing portrait images with affective enhancement.

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Jablonski, Nina G. "The Evolution of Human Skin and Skin Color." Annual Review of Anthropology 33, no. 1 (October 2004): 585–623. http://dx.doi.org/10.1146/annurev.anthro.33.070203.143955.

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Wallace, Marsha D., Neil F. Box, and Gareth L. Bond. "SNPing away at human skin color." Pigment Cell & Melanoma Research 27, no. 3 (March 3, 2014): 322–23. http://dx.doi.org/10.1111/pcmr.12229.

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Relethford, John H. "Hemispheric difference in human skin color." American Journal of Physical Anthropology 104, no. 4 (December 1997): 449–57. http://dx.doi.org/10.1002/(sici)1096-8644(199712)104:4<449::aid-ajpa2>3.0.co;2-n.

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Chaplin, George, and Nina G. Jablonski. "Hemispheric difference in human skin color." American Journal of Physical Anthropology 107, no. 2 (October 1998): 221–23. http://dx.doi.org/10.1002/(sici)1096-8644(199810)107:2<221::aid-ajpa8>3.0.co;2-x.

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Trivedi, Apoorva, and Jinal Gandhi. "The Evolution of Human Skin Color." JAMA Dermatology 153, no. 11 (November 1, 2017): 1165. http://dx.doi.org/10.1001/jamadermatol.2017.3695.

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Subban, Ravi, Pasupathi Perumalsamy, and G. Annalakshmi. "A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation." Applied Mechanics and Materials 743 (March 2015): 317–20. http://dx.doi.org/10.4028/www.scientific.net/amm.743.317.

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This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

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Huang, Hui Ming, He Sheng Liu, and Guo Ping Liu. "Face Image Segmentation Using Color Information and Saliency Map." Applied Mechanics and Materials 55-57 (May 2011): 77–81. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.77.

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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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Johan, Nurul Fatiha, Yasir Mohd Mustafah, and Nahrul Khair Alang Md Rashid. "Human Body Parts Detection Using YCbCr Color Space." Applied Mechanics and Materials 393 (September 2013): 556–60. http://dx.doi.org/10.4028/www.scientific.net/amm.393.556.

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Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

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ZAQOUT, IHAB, ROZIATI ZAINUDDIN, and SAPIAN BABA. "HUMAN FACE DETECTION IN COLOR IMAGES." Advances in Complex Systems 07, no. 03n04 (September 2004): 369–83. http://dx.doi.org/10.1142/s021952590400024x.

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In this paper we have used a simple and efficient color-based approach to segment human skin pixels from background, using a 2D histogram-based approach as a preprocess stage for human face detection. For skin segmentation, a total of 446,007 skin samples from the training set is manually cropped from the RGB color images, to calculate three lookup tables based on the relationship between each pair of the triple components (R, G, B). Derivation of skin classifier rules from the lookup tables are based on how often each attribute value (interval) occurs, and their associated certainty values. For face detection, we assume the face-appearance as blob-like, and that the face has an approximately elliptical shape. Accordingly, an ellipse-fitting algorithm is appropriate, which is based on statistical moments, and those blobs that have an elliptical shape are retained as face candidates.

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images." International Journal of Computer Vision and Image Processing 5, no. 2 (July 2015): 35–57. http://dx.doi.org/10.4018/ijcvip.2015070103.

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Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

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Liang, Yun Juan, Xiao Ying Wu, Li Juan Ma, and Li Jun Zhang. "Face Localization in Color Images Based on Skin Color and Eye Gradient." Advanced Materials Research 268-270 (July 2011): 1382–85. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1382.

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In color images, skin color is the important information on human face. This paper proposes a method to detect and locate human face rapidly based on skin color information and eye gradient. First, normalized RGB space is converted to HSV space; Secondly, the images are pretreated by smoothing and light compensation to overcome the uneven illumination changes, and then the defined skin color model is used to determine candidate regions of the human face, finally the human face is located accurately through eye localization based on gradient template. Experiments show that the method is fast and effective.

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AI, IBRAIMOV. "EVOLUTION OF HUMAN SKIN COLOR AND THERMOREGULATION." International Journal of Genetics 4, no. 3 (November 30, 2012): 111–15. http://dx.doi.org/10.9735/0975-2862.4.3.111-115.

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Barsh, Gregory S. "What Controls Variation in Human Skin Color?" PLoS Biology 1, no. 1 (October 13, 2003): e27. http://dx.doi.org/10.1371/journal.pbio.0000027.

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McElhinney, Doff B., Stephen J. Hoffman, William A. Robinson, and J. a. n. Ferguson. "Effect of Melatonin on Human Skin Color." Journal of Investigative Dermatology 102, no. 2 (February 1994): 258–59. http://dx.doi.org/10.1111/1523-1747.ep12371773.

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Quevedo, Walter C., Thomas B. Fitzpatrick, and Kowichi Jimbow. "Human skin color: Origin, variation and significance." Journal of Human Evolution 14, no. 1 (January 1985): 43–56. http://dx.doi.org/10.1016/s0047-2484(85)80094-4.

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Marguier, J., N. Bhatti, H. Baker, M. Harville, and S. Süsstrunk. "Assessing human skin color from uncalibrated images." International Journal of Imaging Systems and Technology 17, no. 3 (2007): 143–51. http://dx.doi.org/10.1002/ima.20114.

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Tang, San. "Human Face Detection Method Based on Skin Color Model." Advanced Materials Research 706-708 (June 2013): 1877–81. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1877.

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Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results demonstrate that the proposed method is feasible and effective.

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Sobabe, Abdou-Aziz, Tahirou Djara, Blaise Blochaou, and Antoine Vianou. "Soft Biometrics Authentication." Journal of Information Technology Research 15, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/jitr.298620.

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This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.

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BOURBAKIS, N., P. KAKUMANU, S. MAKROGIANNIS, R. BRYLL, and S. PANCHANATHAN. "NEURAL NETWORK APPROACH FOR IMAGE CHROMATIC ADAPTATION FOR SKIN COLOR DETECTION." International Journal of Neural Systems 17, no. 01 (February 2007): 1–12. http://dx.doi.org/10.1142/s0129065707000920.

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The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting — adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to human skin color. The comparison of our method with Gray World, White Patch and NN on White Patch methods for skin color stabilization is presented. The skin regions in the NN stabilized images are successfully detected using a computationally inexpensive thresholding operation. We also present results on detecting skin regions on a data set of test images. The results are promising and suggest a new approach for adapting human skin color using neural networks.

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Cao, Xin Yan, and Hong Fei Liu. "A Skin Detection Algorithm Based on Bayes Decision in the YCbCr Color Space." Applied Mechanics and Materials 121-126 (October 2011): 672–76. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.672.

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Skin color detection is a hot research of computer vision, pattern identification and human-computer interaction. Skin region is one of the most important features to detect the face and hand pictures. For detecting the skin images effectively, a skin color classification technique that employs Bayesian decision with color statistics data has been presented. In this paper, we have provided the description, comparison and evaluation results of popular methods for skin modeling and detection. A Bayesian approach to skin color classification was presented. The statistics of skin color distribution were obtained in YCbCr color space. Using the Bayes decision rule for minimum cot, the amount of false detection and false dismissal could be controlled by adjusting the threshold value. The results showed that this approach could effectively identify skin color pixels and provide good coverage of all human races, and this technique is capable of segmenting the hands and face quite effectively. The algorithm allows the flexibility of incorporating additional techniques to enhance the results.

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Costin, Gertrude-E., and Vincent J. Hearing. "Human skin pigmentation: melanocytes modulate skin color in response to stress." FASEB Journal 21, no. 4 (January 22, 2007): 976–94. http://dx.doi.org/10.1096/fj.06-6649rev.

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Yokota, Tomoyuki, Peter Zalar, Martin Kaltenbrunner, Hiroaki Jinno, Naoji Matsuhisa, Hiroki Kitanosako, Yutaro Tachibana, Wakako Yukita, Mari Koizumi, and Takao Someya. "Ultraflexible organic photonic skin." Science Advances 2, no. 4 (April 2016): e1501856. http://dx.doi.org/10.1126/sciadv.1501856.

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Thin-film electronics intimately laminated onto the skin imperceptibly equip the human body with electronic components for health-monitoring and information technologies. When electronic devices are worn, the mechanical flexibility and/or stretchability of thin-film devices helps to minimize the stress and discomfort associated with wear because of their conformability and softness. For industrial applications, it is important to fabricate wearable devices using processing methods that maximize throughput and minimize cost. We demonstrate ultraflexible and conformable three-color, highly efficient polymer light-emitting diodes (PLEDs) and organic photodetectors (OPDs) to realize optoelectronic skins (oe-skins) that introduce multiple electronic functionalities such as sensing and displays on the surface of human skin. The total thickness of the devices, including the substrate and encapsulation layer, is only 3 μm, which is one order of magnitude thinner than the epidermal layer of human skin. By integrating green and red PLEDs with OPDs, we fabricate an ultraflexible reflective pulse oximeter. The device unobtrusively measures the oxygen concentration of blood when laminated on a finger. On-skin seven-segment digital displays and color indicators can visualize data directly on the body.

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Zhao, Jing Ying, Xiao Dong Duan, and Hai Guo. "Design and Implementation of a Face Location and Five Sense Organs Marking Software." Advanced Materials Research 831 (December 2013): 490–94. http://dx.doi.org/10.4028/www.scientific.net/amr.831.490.

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Face recognition technology is a significant branch of the study of artificial intelligence, the recognition precision is easily affected by facial expressions, skin colors, beam angles in the images and apparels. This essay tests human face images in the format of 24 BMP and realizes face location and mark of five sense organs. Firstly, color space model is adopted to set up skin color distribution model to segment skin regions; secondly, the obtained regions are judged and screened preliminarily, and optimized based on the characteristics of segmented regions with region optimization algorithm of depth-width ratio, rejecting the region with the similar color of the skin caused by some disturbing factors and other naked parts of the body, through which the rough region of human face could be attained and face location could be realized; finally, five organs of the obtained face region is located with the method of grey level region in combination with searching rectangle.

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WooJang, Seok, and Siwoo Byun. "Facial region detection robust to changing backgrounds." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 25. http://dx.doi.org/10.14419/ijet.v7i2.12.11028.

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Background/Objectives: These days, many studies have actively been conducted on intelligent robots capable of providing human friendly service. To make natural interaction between humans and robots, it is required to develop the mobile robot-based technology of detecting human facial regions robustly in dynamically changing real backgrounds.Methods/Statistical analysis: This paper proposes a method for detecting facial regions adaptively through the mobile robot-based monitoring of backgrounds in a dynamic real environment. In the proposed method, a camera-object distance and a color change in object background are monitored, and thereby the skin color extraction algorithm most suitable for the measured distance and color is applied. In the face detection step, if the searched range is valid, the most suitable skin color detection method is selected so as to detect facial regions.Findings: To sum up the experimental results, algorithms have a difference in performance depending on a distance and a background color. Overall, the algorithms using neural network showed stable results. The algorithm using Kismet had a good perception rate for the ground truth part of an original image, and a skin color detection rate was greatly influenced by pink and yellow background colors similar to a skin tone, and consequently an incorrect perception rate of background was considerably high. With regard to each algorithm performance depending on a distance, the closer a distance with an object was to 320cm, the more an incorrect perception rate of a background sharply increased. To analyze the performance of each skin color detection algorithm applied to face detection, we examined how much a skin color of an original image was detected by each algorithm. For a skin color detection rate, after the ground truth for the skin of an original image, the number of pixels of the skin color detected by each algorithm was calculated. In this case, the ground truth means a range of the skin color of an original image to detect.Improvements/Applications: We expect that the proposed approach of detecting facial regionsin a dynamic real environment will be used in a variety of application areas related to computer vision and image processing.

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Han, Yan Bin, and Gang Song. "Skin Color Protection Based on Wide Gamut Display." Advanced Materials Research 926-930 (May 2014): 3559–62. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3559.

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Gamut extension algorithm transforms a picture to display in wide gamut, exerting a good performance. However, human skin color belongs to static color, if extended, it would look unnatural and affect the beauty of the image. So we presents an algorithm for protecting skin color during color gamut extension. Firstly, we initially identify skin color regions. Then, according to probabilistic model, a new method that we use it to avoid looking hair as face skin with similar color. Experimental results demonstrate that our proposed method does protect skin color and improve performance.

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Barsh, Gregory S. "Correction: What Controls Variation in Human Skin Color?" PLoS Biology 1, no. 3 (December 22, 2003): e91. http://dx.doi.org/10.1371/journal.pbio.0000091.

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Wang, Zhi Wen, and Shao Zi Li. "Face Recognition Based on Template Matching and Skin-Color Segmentation." Advanced Materials Research 271-273 (July 2011): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.165.

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In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and is vulnerable to the impact of background which is similar with skin-color, face recognition algrithom based on skin color segmentation and template matching is presented in this paper. According to the clustering properties that the skin-color of human faces have emerged in the YCbCr color space, the regions closing to facial skin color are separated from the image by using Gaussian mixture model in order to achieve the purpose of rapidly detecting the external face of human face. Adaptive template matching is used to overcome the affect of the backgrounds which are similar with skin color on face recognition. Computation in the matching process is reduced by using the second matching algorithm. Extraction of face images by using singular value features is used to identify faces and to reduce the dimensions of the eigenvalue matrix in the course of facial feature extraction. Experimental results show that proposed method can rapidly recongnise human faces, and improve the accuracy of face recognition.

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BONVENTI, WALDEMAR, and ANNA HELENA REALI COSTA. "HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 06 (September 2008): 1241–65. http://dx.doi.org/10.1142/s0218001408006739.

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In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised- and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

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Rosenstock Gonzalez, Yael R., Deana Williams, and Debby Herbenick. "Skin Color and Skin Tone Diversity in Human Sexuality Textbook Anatomical Diagrams." Journal of Sex & Marital Therapy 48, no. 3 (October 14, 2021): 285–94. http://dx.doi.org/10.1080/0092623x.2021.1989533.

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Maitra, Sumit, Diptendu Chatterjee, and Arup Ratan Bandyopadhyay. "Skin color variation: A study on Eastern and North East India." Asian Journal of Medical Sciences 10, no. 3 (May 1, 2019): 13–16. http://dx.doi.org/10.3126/ajms.v10i3.23256.

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Background: Skin pigmentation is one of the most variable phenotypic traits and most noticeable of human polymorphisms. Skin pigmentation in humans is largely determined by the quantity and distribution of the pigment melanin. The literature review on skin color variation revealed a few works on skin pigmentation variation has been conducted in India from Southern, Western and Northern part. Aims and Objectives: To best of the knowledge, the present discourse is the first attempt to understand skin color variation from Eastern and North Eastern part of India among three populations. Materials and Methods: The present study consisted of 312 participants from Chakma and Tripuri groups of Tripura, North East India, and participants from Bengalee Hindu caste population from West Bengal. Skin color was measured by Konica Minolta CR-10 spectrophotometer which measures and quantifies the colors with a 3D color space (CIELAB) color space created by 3 axes. All the skin color measurements from each participant were taken from unexposed (underarm) left and right to get a mean and exposed (forehead) to sunlight. Results: The distribution of skin color variation among the three populations demonstrated significant (p<0.05) difference in lightness for unexposed and exposed indicating lightness in unexposed area. Furthermore, the present study revealed significant difference (p<0.05) in skin color among the ethnic groups across the body location and all three attributes (lightness, redness and yellowness). Conclusion: Generally, skin color variation may be elucidated by two main factors: individual differences in lightness and yellowness and by and large due to ethnicity, where diversity in redness is due to primarily due to different body locations. Variation in lightness have more characteristic probability. The present study first time reports the wide range of quantitative skin color variation among the three ethnic groups from Eastern and North East India and highest yellowness (b*) among the population from North East India.

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Yoshii, Junki, Shoji Yamamoto, Kazuki Nagasawa, Wataru Arai, Satoshi Kaneko, Keita Hirai, and Norimichi Tsumura. "Estimation of Layered Ink Layout from Arbitrary Skin Color and Translucency in Inkjet 3D Printer." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 177–82. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.32.

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In this paper, we propose a layout estimation method for multi-layered ink by using PSF measurement and machine learning. This estimation can bring various capabilities of color reproduction for the newfangled 3D printer that can apply multi-layered inkjet color. Especially, the control of translucency is useful for the reproduction of skin color that is overpainted flesh color on bloody-red layer. Conventional method of this layer design and color selection depended on the experience of professional designer. However, it is difficult to optimize the color selection and layer design for reproducing complex colors with many layers. Therefore, in this research, we developed an efficiency estimation of color layout for human skin with arbitrary translucency by using machine learning. Our proposed method employs PSF measurement for quantifying the color translucency of overlapped layers. The machine learning was performed by using the correspondence between these measured PSFs and multi-layered printings with 5-layer neural network. The result was evaluated in the CG simulation with the combination of 14 colors and 10 layers. The result shows that our proposed method can derive an appropriate combination which reproduce the appearance close to the target color and translucency.

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Wang, Yu Zhao, Ming Ronnier Luo, Safdar Muhammad, Hai Yan Liu, and Xiao Yu Liu. "Physical Measurement and Spectral Reproduction of Human Skin Color." Applied Mechanics and Materials 731 (January 2015): 13–17. http://dx.doi.org/10.4028/www.scientific.net/amm.731.13.

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This work forms part of large project for measuring the skin colors. This topic has been historically extensively studied due to the strong need from the photographic, digital imaging and medical applications. However there are still many unresolved issues, for example the measuring accuracy and the difference between different measuring methods. The paper focused on one of the measuring methods: camera. The goal is to develop PCA methods to reconstruct the reflectance from images captured by a camera, and the result shows that using three components is enough to acquire high accuracy, and it is possible to have a single skin model to predict all the available skin colors.

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Agnihotri, Vandana. "HUMAN LIFE AND DIVERSE COLORS." International Journal of Research -GRANTHAALAYAH 2, no. 3SE (December 31, 2014): 1–3. http://dx.doi.org/10.29121/granthaalayah.v2.i3se.2014.3652.

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If there were no colors, it is difficult to say what the form of the world would be like. Colors affect the living beings. Man is a man of knowledge. Even birds and insects are attracted to colors, but not all colors have the same effect. All colors are beautiful and unpleasant. It depends on the attitude of the person to see which color they like. There is no definition of color. Color is the coating of an outer skin. This coating depends on the anatomy and environment. Man's fair complexion (white, pink, dark), black (dark, excessive black) depends on his anatomy and environment. Where there is strong heat, people are black and where there is cold and icy areas, people are white. The skin also has a proper effect on the environment, air and air and anatomy. Some organisms have an immediate effect on the surrounding environment and their body adapts to that environment at the same time as the chameleon. Some people like one color, others are irritated with it but in some animals birds also like this Feedback is seen. The idea of terrorizing or instigating wild animals with red color is famous. Chidiya and kida makode are also seen as enamored and disgusted with different colors. रंग न होते तो संसार का रूप कैसा होता कहना कठिन है। रंग प्राणीमात्र को प्रभावित करते है। मनुष्य तो खैर ज्ञान का पुतला है। पषु-पक्षी और कीट पतंगो तक रंगो के प्रति आकर्षित रहते है किन्तु सभी रंग एक जैसा प्रभाव नही डालते। सभी रंग शोभन भी हैं और अप्रिय भी। यह देखने वाले की मनोवृत्ति पर निर्भर हैं कि किसे कौनसा रंग प्रिय लगता है।रंग की कोई परिभाषा नही है। रंग एक बाहरी त्वचा का लेपमात्र है शरीर रचना और पर्यावरण पर यह लेप निर्भर करता है। मनुष्य का गोरा रंग (सफेद, गुलाबी, गंदुमी) होना, काला (सांवला, अत्यधिक काला) होना उनकी शारीरिक रचना और पर्यावरण पर निर्भर करता है। जहां तेज गर्मी होती है वहां लोग काले एवं जहां ठण्डे एवं बर्फीले इलाके होते है वहां लोग गोरे होते है। त्वचा पर भी वातावरण, आबो-हवा और शारीरिक रचना का समुचित प्रभाव पडता है। कुछ जीवों पर आसपास के वातावरण का तत्काल प्रभाव प्रडता है और उनका शरीर उसी क्षण उस वातावरण के अनुकूल हो जाता है जैसे गिरगिट।कुछ लोग एक रंग पसंद करते है तो दूसरों को उससे चिढ़ है परंतु कुछ पषु-पक्षियों में भी रंगो के प्रति यही प्रतिक्रिया देखी जाती है। लाल रंग से जंगली जानवारों के आतंकित होने या भड़कने की बाते प्रसिद्ध है। चिडिया और कीडे मकोडे भी विभिन्न रंगो के प्रति आसक्त और विरक्त देखे जाते है।

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Wu, Xuan Run, and Jian Da Cao. "The Choice of the Simulation Skin in the Experiment of Dynamic Moisture Transfer Based on Munsell Color Index." Advanced Materials Research 335-336 (September 2011): 912–15. http://dx.doi.org/10.4028/www.scientific.net/amr.335-336.912.

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The function of the simulation sweating device when done the experiment of dynamic moisture transfer based on Munsell color index was needed to simulate the state of the human body sweating as far as possible, the performance of the simulation skin would directly affect the stability of experimental results. This paper by selecting the four kinds of materials as an alternative of the simulation skin had carried out the experiment of dynamic moisture transfer based on Munsell color index and had done the analysis of variance of experimental results, and had found that results were significantly different when using different simulation skins. Further had done the test of the preserving water rate of seven representative materials, test results show that: the 2 # simulated skin 95% cotton knitted +fabric 5% op had the best preserving water performance, the 2 # simulated skin 95% cotton knitted +fabric 5% op could been chosen as the outer layer of simulated skin .the 6 #, faux suede had better similarity with morphology of human skin tissue. So, it could be chosen as in the layer of the simulated skin.

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Osman, Ghazali, and Muhammad Suzuri Hitam. "Skin Color detection Using Stepwise Neural Network and Color Mapping Co-occurrence Matrix." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 7 (February 23, 2014): 3642–50. http://dx.doi.org/10.24297/ijct.v12i7.3076.

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Skin color has been proven to be a useful and robust cue for face detection, human tracking, image content filtering, pornographic filtering, etc.Â Most of skin classification researches are focused on using pixel-based method to classify skin and non-skin pixels.Â This paper proposed a new technique for region-based skin color detection using texture information.Â The texture information was extracted from the color mapping co-occurrence matrix (CMCM).Â This technique is extension of gray level co-occurrence matrix (GLCM) which is introduced by Haralicket. al to compute second order statistical texture features.Â The new color mapping matrix (CMM) between color bands have been developed for skin and non-skin area for each skin image and then, the CMCM were computed at four direction with distance, d = 1, and angle, Î¸ = 0o, 45o, 90o, and 135o.Â The thirteen Haralickâ€™s textures have been computed and used for formulating a skin color classifiers using stepwise neural network (SNN).Â The performance of each skin color classifier was measured based on true and false positive value.Â Besides that, the benchmark datasets from Universidad de Chile and TDSD were also be employed to test the skin color classifiers ability.Â The results shown that the skin color classifier formulated with [RGB] CMCM at direction (1, 0o) most superior as compared to other direction.Â Its average of true positive and false positive are 98.38 percent and 3.67 percent, respectively.Â Meanwhile, the classifier formulated with [RGB] CMCM at direction (1, 90o) is totally failed to classify skin and non-skin colors.Â Meaning that, the texture features which are computed from [RGB] CMCM at direction (1, 90o) cannot represent skin and non-skin color at all.

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Li, Yan, and Hongmei Liu. "Study on A face detection method based on elliptic skin color model." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 52–56. http://dx.doi.org/10.54097/hset.v7i.995.

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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.

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Balter, M. "GENETICS: Zebrafish Researchers Hook Gene for Human Skin Color." Science 310, no. 5755 (December 16, 2005): 1754a—1755a. http://dx.doi.org/10.1126/science.310.5755.1754a.

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Thorstenson, Christopher A., Adam D. Pazda, and Andrew J. Elliot. "Social Perception of Facial Color Appearance for Human Trichromatic Versus Dichromatic Color Vision." Personality and Social Psychology Bulletin 46, no. 1 (April 13, 2019): 51–63. http://dx.doi.org/10.1177/0146167219841641.

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Typical human color vision is trichromatic, on the basis that we have three distinct classes of photoreceptors. A recent evolutionary account posits that trichromacy facilitates detecting subtle skin color changes to better distinguish important social states related to proceptivity, health, and emotion in others. Across two experiments, we manipulated the facial color appearance of images consistent with a skin blood perfusion response and asked participants to evaluate the perceived attractiveness, health, and anger of the face (trichromatic condition). We additionally simulated what these faces would look like for three dichromatic conditions (protanopia, deuteranopia, tritanopia). The results demonstrated that flushed (relative to baseline) faces were perceived as more attractive, healthy, and angry in the trichromatic and tritanopia conditions, but not in the protanopia and deuteranopia conditions. The results provide empirical support for the social perception account of trichromatic color vision evolution and lead to systematic predictions of social perception based on ecological social perception theory.

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Ye, Qing. "Research of Face Detection Method Based on Skin Color Feature." Applied Mechanics and Materials 373-375 (August 2013): 478–82. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.478.

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Human face detection is the first critical step of face recognition system. This paper proposed a face detection method based on skin color feature. Firstly, the method of building a skin color feature from RGB to YCbCr and extracting skin color region according the chrominance similarity was used to extract the face gray image. Secondly, image smoothness and image binarization were used to receive the binary image, then mathematical morphology operators were used to eliminate the binary images noise and disturbance. At last, human face regions are detected through projection operation. The result of experimentation affirms that the method is efficient to detect human face.

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Soundharya, Ms R. "Skin Disease Detection Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 4146–53. http://dx.doi.org/10.22214/ijraset.2024.62566.

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Abstract: The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis

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Kittles, Rick A., Eunice R. Santos, Nefertiti S. Oji-Njideka, and Carolina Bonilla. "Race, Skin Color and Genetic Ancestry." Californian Journal of Health Promotion 5, SI (May 1, 2007): 9–23. http://dx.doi.org/10.32398/cjhp.v5isi.1195.

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Defining race continues to be a nemesis. Knowledge from human genetic research continuously challenges the notion that race and biology are inextricably linked, with implications across biomedical and public health disciplines. While it has become fashionable for scientists to declare that race is merely a social construction, there is little practical value to this belief since few in the public believe and act on it. In the U.S., race has largely been based on skin color and ancestry, both of which exhibit large variances within communities of color. Yet biomedical studies continue to examine black / white group differences in health. Here we discuss why using race in biomedical studies is problematic using examples from two U.S. groups (African and Hispanic Americans) which transcend ‘racial’ boundaries and bear the burden of health disparities.

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Rismayana, Aris Haris, Henny Alfianti, and Dadan Saepul Ramdan. "Facial Skin Color Segmentation Using Otsu Thresholding Algorithm." Journal of Applied Intelligent System 7, no. 1 (May 19, 2022): 26–35. http://dx.doi.org/10.33633/jais.v7i1.5513.

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The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.

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Choi, Hayan, Kyungah Choi, and Hyeon-Jeong Suk. "Performance of the 14 skin-colored patches in accurately estimating human skin color." Electronic Imaging 2017, no. 17 (January 29, 2017): 62–65. http://dx.doi.org/10.2352/issn.2470-1173.2017.17.coimg-424.

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D C, Dr Shubhangi. "Human Skin Cancer Recognition and Classification by Unified Skin Texture and Color Features." IOSR Journal of Computer Engineering 12, no. 4 (2013): 42–49. http://dx.doi.org/10.9790/0661-1244249.

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Putra, Leonardus Sandy Ade, Vincentius Abdi Gunawan, and Agus Sehatman Saragih. "Detection of Actinic Keratosis Skin Cancer Using Gray Level Co-occurrence Matrix Texture Extraction and Color Extraction With Support Vector Machine Classification." TEKNIK 44, no. 2 (August 20, 2023): 158–66. http://dx.doi.org/10.14710/teknik.v44i2.44895.

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Nowadays, humans tend to carry out activities during the day, both indoors and outdoors. Activities carried out outdoors cause human skin to often receive direct exposure to sunlight, which contains ultraviolet (UV) rays. Direct exposure to UV rays on the skin will harm the skin's health, which is the covering of the human body. Harmful effects on the skin usually include the skin becoming dark and dull, burns, and even causes cancer. One of the skin cancers that may appear on human skin is Actinic Keratosis (AK) cancer. AK cancer is a type of cancer that is classified as benign and can be cured with medical help. However, if this cancer is not caught early, it can become Squamous Cell Carcinoma (SCC), a type of malignant cancer. This research aims to design a system for identifying AK cancer types using color and texture feature extraction. RGB color feature extraction is obtained from image color segmentation and RGB values. The Gray Level Co-occurrence Matrix (GLCM) method is used to determine the texture of the skin cancer. Identification is carried out by a classification process using a Support Vector Machine (SVM), which can recognize the type of AK cancer. This research uses three classification methods: classification with color extraction, classification with texture extraction, and classification with color and texture extraction. Research shows that the highest level of accuracy in cancer recognition reaches 96% by combining color and texture extraction results as classification determinants. So, the system designed has succeeded in recognizing the type of AK cancer early on..

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Chen, Liang Hwa, Po Lun Chang, Guo Wei Lin, and Yen Ching Chang. "Intelligent Human Eye State Identification Based on 2DPCA and Skin Color." Applied Mechanics and Materials 145 (December 2011): 252–56. http://dx.doi.org/10.4028/www.scientific.net/amm.145.252.

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Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D)2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.

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Journal articles on the topic 'Human skin color'

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