Relevant bibliographies by topics / Human skin color

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Human skin color'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "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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Dissertations / Theses on the topic "Human skin color"

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Stephen, Ian D. "Skin colour, pigmentation and the perceived health of human faces." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/753.

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Iliescu, Florin Mircea. "Unravelling the genetics of human pigmentation in India." Thesis, University of Cambridge, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709532.

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Faria, Rodrigo Augusto Dias. "Human skin segmentation using correlation rules on dynamic color clustering." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-01102018-101814/.

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Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images.
A pele humana é constituída de uma série de camadas distintas, cada uma das quais reflete uma porção de luz incidente, depois de absorver uma certa quantidade dela pelos pigmentos que se encontram na camada. Os principais pigmentos responsáveis pela origem da cor da pele são a melanina e a hemoglobina. A segmentação de pele desempenha um papel importante em uma ampla gama de aplicações em processamento de imagens e visão computacional. Em suma, existem três abordagens principais para segmentação de pele: baseadas em regras, aprendizado de máquina e híbridos. Elas diferem em termos de precisão e eficiência computacional. Geralmente, as abordagens com aprendizado de máquina e as híbridas superam os métodos baseados em regras, mas exigem um conjunto de dados de treinamento grande e representativo e, por vezes, também um tempo de classificação custoso, que pode ser um fator decisivo para aplicações em tempo real. Neste trabalho, propomos uma melhoria, em três versões distintas, de um novo método de segmentação de pele baseado em regras que funciona no espaço de cores YCbCr. Nossa motivação baseia-se nas hipóteses de que: (1) a regra original pode ser complementada e, (2) pixels de pele humana não aparecem isolados, ou seja, as operações de vizinhança são levadas em consideração. O método é uma combinação de algumas regras de correlação baseadas nessas hipóteses. Essas regras avaliam as combinações de valores de crominância Cb, Cr para identificar os pixels de pele, dependendo da forma e tamanho dos agrupamentos de cores de pele gerados dinamicamente. O método é muito eficiente em termos de esforço computacional, bem como robusto em imagens muito complexas.
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Barros, Renan Sales. "Simulation of human skin pigmentation disorders." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/78876.

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Nosso trabalho apresenta um modelo de simulação de transtornos de pigmentação humana. Nosso modelo é formado por um conjunto de equações diferenciais que definem um sistema reação-difusão. Nosso sistema simula algumas características do sistema pigmentar humano. Alterações nesse sistema podem levar a desequilíbrios na distribuição de melanina na pele resultando em artefatos conhecidos como lesões de pigmentação. Nosso modelo tem como objetivo reproduzir essas alterações e assim sintetizar lesões de pigmentação humanas. Nosso sistema reação-difusão foi elaborado tomando como base dados biológicos a respeito da pele humana, do sistema pigmentar e do ciclo de vida dos melanócitos, que são as principais células envolvidas nesse tipo de transtorno. A simulação desse tipo de transtorno apresenta diversas aplicações em dermatologia como, por exemplo, suporte para o treinamento de dermatologistas e auxílio no diagnóstico de transtornos de pigmentação. No entanto, nosso trabalho se concentra em aplicações relacionadas com computação gráfica. Assim, nós também apresentamos um método para transferir os resultados do nosso modelo de simulação para texturas e imagens de pele humana. Nesse contexto, o nosso modelo contribui para a geração de texturas de pele mais realistas e consequentemente para a geração de modelos de serem humanos mais realistas. Além disso, nós também comparamos os resultados da nossa simulação com lesões de pigmentações reais objetivando avaliar a qualidade das lesões geradas pelo nosso modelo. Para realizar essa comparação nós extraímos métricas das lesões sintetizadas e das lesões reais e comparamos os valores dessas métricas. Com base nessa comparação, nós observamos que as lesões sintetizadas apresentam as mesmas características das lesões reais. Ainda, para efeito de comparações visuais, nós também apresentamos imagens de lesões reais lado a lado com imagens sintetizadas e podemos observar que o método utilizado para produzir imagens de lesões a partir do resultado do nosso modelo de simulação produz resultados que são indistinguíveis das imagens reais.
Our work presents a simulation model of human pigmentation disorders. Our model is formed by a set of differential equations that defines a reaction-diffusion system. Our system simulates some features of the human pigmentary system. Changes in this system can lead to imbalances in the distribution of melanin in the skin resulting in artifacts known as pigmented lesions. Our model aims to reproduce these changes and consequently synthesize human pigmented lesions. Our reaction-diffusion system was developed based on biological data regarding human skin, pigmentary system and melanocytes life cycle. The melanocytes are the main cells involved in this type of human skin disorders. The simulation of such disorders has many applications in dermatology, for example, to assist dermatologists in diagnosis and training related to pigmentation disorders. However, our study focuses on applications related to computer graphics. Thus, we also present a method to transfer the results of our simulation model for textures and images of human skin. In this context, our model contributes to the generation of more realistic skin textures and consequently for the generation of more realistic human models. Moreover, we also compared the results of our simulation with real pigmented lesions to evaluate the quality of the lesions generated by our model. To perform this comparison we measured some features of real and synthesized pigmented lesions and we compared the results of these measurements. Based on this comparison, we observed that synthesized lesions exhibit the same characteristics of real lesions. Still, for the purpose of visual comparisons, we also present images of real lesions along with images of synthesized lesions. In this visual comparison, we can note that the method used to produce lesions images from the results of our simulation generates images that are indistinguishable from real images.
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PORTER, CORNELIA PAULINE. "SOCIALIZATION, BLACK SCHOOL-AGE CHILDREN AND THE COLOR CASTE HIERARCHY (SOCIAL COGNITION, PSYCHOLOGY, NURSING)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/188010.

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The purpose of the descriptive research was to investigate the relationship between an adherence to the Black community's belief and value system about Black skin tones and Black school-age children's skin tone preferences and perceptions of occupational life opportunities. Six Black skin tones were scaled via Thurstone's method of paired comparisons and the law of comparative judgment. The result was an interval level Skin Tone Scale on which the skin tones were positioned from most to least preferred by the children. The most preferred skin tones ranged from medium to honey brown. The least preferred were the extreme tones of very light yellow and very dark brown. Data collection was accomplished with the Porter Skin Tone Connotation Scale (PSTCS). The instrument was constructed from the forced choice preference paradigm. Data were obtained from a volunteer sample of 98 Black school-age children who resided in a city in Arizona. Data collection and analyses were constructed to test two hypotheses: (1) Black school-age children's skin tone classifications for differential status occupations will be related to gender, age, and perception of own skin tone as indexed by the skin tone values of the Skin Tone Scale, and (2) with increasing age, Black school-age children's skin tone preferences will be more systematically related to the skin tone values of the Skin Tone Scale. Testing of the first hypothesis with multiple regression indicated that the independent variables did not account for enough variance to support the hypothesis. Analysis of the second hypothesis with coefficient gamma suggested a trend toward more systematic agreement with the Skin Tone Scale with increasing age. Results of the first hypothesis were discussed in relation to composition of the sample, gender differences, the achievement value of the Black sociocultural system, and these Black children's lived experience. Results of the second hypothesis reflected those from similar investigations conducted in the 1940s. The results suggested Black children still most prefer brown skin tones and least prefer extreme light and dark skin tones. Black children's preferences for Black skin tones have not altered in approximately forty years.
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Gosin, Monika. "(Re) framing the nation the Afro-Cuban challenge to Black and Latino struggles for American identity /." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3355784.

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Thesis (Ph. D.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed June 25, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 296-311).
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Natividad, Beverly Romero. "Rendering whiteness visible in the Filipino culture through skin-whitening cosmetic advertisements." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/2974.

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Clemens, Alexander. "Investigating the Inclusivity of Face Detection." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1836.

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Face detection refers to a number of techniques that identify faces in images and videos. As part of the senior project exercise at Pomona College, I explore the process of face detection using a JavaScript library called CLMtrackr. CLMtrackr works in any browser and detects faces within the video stream captured by a webcam. The focus of this paper is to explore the shortcomings in the inclusivity of the CLMtrackr library and consequently that of face detection. In my research, I have used two datasets that contain human faces with diverse backgrounds, in order to assess the accuracy of CLMtrackr. The two datasets are the MUCT and PPB. In addition, I investigate whether skin color is a key factor in determining face detection's success, to ascertain where and why a face might not be recognized within an image. While my research and work produced some inconclusive results due to a small sample size and a couple outliers in my outputs, it is clear that there is a trends toward the CLMtrackr algorithm recognizing faces with lighter skin tones more often than darker ones.
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O'Mara, David Thomas John. "Automated facial metrology." University of Western Australia. School of Computer Science and Software Engineering, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0015.

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Automated facial metrology is the science of objective and automatic measurement of the human face. There are many reasons for measuring the human face. Psychologists are interested in determining how humans perceive beauty, and how this is related to facial symmetry [158]. Biologists are interested in the relationship between symmetry and biological fitness [124]. Anthropologists, surgeons, forensic experts, and security professionals can also benefit from automated facial metrology [32, 101, 114]. This thesis investigates the concept of automated facial metrology, presenting original techniques for segmenting 3D range and colour images of the human head, measuring the bilateral symmetry of n-dimensional point data (with particular emphasis on measuring the human head), and extracting the 2D profile of the face from 3D data representing the head. Two facial profile analysis techniques are also presented that are incremental improvements over existing techniques. Extensive literature reviews of skin colour modelling, symmetry detection, symmetry measurement, and facial profile analysis are also included in this thesis. It was discovered during this research that bilateral symmetry detection using principal axes is not appropriate for detecting the mid-line of the human face. An original mid-line detection technique that does not use symmetry, and is superior to the symmetry-based technique, was developed as a direct result of this discovery. There is disagreement among researchers about the effect of ethnicity on skin colour. Some researchers claim that people from different ethnic groups have the same skin chromaticity (hue, saturation) [87, 129, 206], while other researchers claim that different ethnic groups have different skin colours [208, 209]. It is shown in this thesis that people from apparently different ethnic groups can have skin chromaticity that is within the same Gaussian distribution. The chromaticity-based skin colour model used in this thesis has been chosen from the many models previously used by other researchers, and its applicability to skin colour modelling has been justified. It is proven in this thesis that the Mahalanobis distance to the skin colour distribution is Gaussian in both the chromatic and normalised rg colour spaces. Most facial profile analysis techniques use either tangency or curvature to locate anthropometric features along the profile. Techniques based on both approaches have been implemented and compared. Neither approach is clearly superior to the other, but the results indicate that a hybrid technique, combining both approaches, could provide significant improvements. The areas of research most relevant to facial metrology are reviewed in this thesis and original contributions are made to the body of knowledge in each area. The techniques, results, literature reviews, and suggestions presented in this thesis provide a solid foundation for further research and hopefully bring the goal of automated facial metrology a little closer to being achieved.
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Judilla, Judy Fondales. "Introduction to cosmetology: Color seasons and palettes." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1757.

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Books on the topic "Human skin color"

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Paul, Sharad P. Skin: A biography. Noida: Fourth Estate, 2013.

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Fox, Carles Lalueza i. El color sota la pell. Barcelona: Rubes, 2003.

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Robins, Ashley H. Biological perspectives on human pigmentation. Cambridge [England]: Cambridge University Press, 1991.

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Rogers, Spencer Lee. The colors of mankind: The range and role of human pigmentation. Springfield, Ill., U.S.A: Thomas, 1990.

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Davis, Donald H. 1954. The powers of black skin pigmentation: A psychological journey into the epidermis skin. Kingston, Jamaica, W.I: Conscious Movement Publication, 1994.

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Alexis, Andrew F., and Victoria Holloway Barbosa. Skin of color: A practical guide to dermatologic diagnosis and treatment. New York: Springer, 2013.

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Paul, Kelly A., and Taylor Susan C, eds. Dermatology for skin of color. New York: McGraw-Hill, 2008.

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Wright, Thomas A. Erase ethnic color labels. Ocala, Fla: Special Publications, 1994.

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Booker, Thomishia. My brown skin. Middleton, Delaware: CreateSpace Independent Publishing Platform, 2017.

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Kissinger, Katie. All the colors we are: The story of how we get our skin color. St. Paul, MN: Redleaf Press, 1994.

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Book chapters on the topic "Human skin color"

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Yang, Ming-Hsuan, and Narendra Ahuja. "Skin Color Model." In Face Detection and Gesture Recognition for Human-Computer Interaction, 83–95. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7_4.

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Isales, Maria Cristina, Timothy Tan, and Lily Marsden. "Skin." In Color Atlas of Human Fetal and Neonatal Histology, 377–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11425-1_34.

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Strickland, Amanda, and Gabriela Blanco. "Human Papillomavirus (HPV)." In Dermatology Atlas for Skin of Color, 201–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54446-0_34.

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Dawood, Gamal. "Skin and Soft Tissues." In Color Atlas of Human Gross Pathology, 149–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91315-1_12.

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Amani, Mahdi, Håvard Falk, Oliver Damsgaard Jensen, Gunnar Vartdal, Anders Aune, and Frank Lindseth. "Color Calibration on Human Skin Images." In Lecture Notes in Computer Science, 211–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34995-0_20.

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Khanam, Ruqaiya, Prashant Johri, and Mario José Diván. "Human Skin Color Detection Technique Using Different Color Models." In Trends and Advancements of Image Processing and Its Applications, 261–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75945-2_14.

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Cavalcanti, Pablo G., Jacob Scharcanski, and Carlos B. O. Lopes. "Shading Attenuation in Human Skin Color Images." In Advances in Visual Computing, 190–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_19.

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Doi, Motonori, Akira Kimachi, Shogo Nishi, and Shoji Tominaga. "Human Skin Color Simulator Using Active Illumination." In Lecture Notes in Computer Science, 75–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20404-3_6.

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Subban, Ravi, and Richa Mishra. "Human Skin Segmentation in Color Images Using Gaussian Color Model." In Advances in Intelligent Systems and Computing, 13–21. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01778-5_2.

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Xu, Tao, Yunhong Wang, and Zhaoxiang Zhang. "Towards Independent Color Space Selection for Human Skin Detection." In Advances in Multimedia Information Processing – PCM 2012, 337–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_31.

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Conference papers on the topic "Human skin color"

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Angelopoulou, Elli. "Understanding the color of human skin." In Photonics West 2001 - Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2001. http://dx.doi.org/10.1117/12.429495.

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Boaventura, I. A. G., V. M. Volpe, I. N. da Silva, and A. Gonzaga. "Fuzzy Classification of Human Skin Color in Color Images." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.385112.

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Shehadeh, Hakam, Audai Al-khalaf, and Mahmood Al-khassaweneh. "Human face detection using skin color information." In 2010 IEEE International Conference on Electro/Information Technology (EIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/eit.2010.5612128.

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Utz, Sergei R., Peter Knuschke, Albert H. Mavlyutov, Helena A. Pilipenko, and Yurii P. Sinichkin. "In vivo human skin autofluorescence: color perception." In BiOS Europe '96, edited by Hans-Jochen Foth, Renato Marchesini, and Halina Podbielska. SPIE, 1996. http://dx.doi.org/10.1117/12.260646.

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Tanaka, Satomi, Akihiro Kakinuma, Naohiro Kamijo, Hiroshi Takahashi, and Norimichi Tsumura. "Illuminant color estimation based on pigmentation separation from human skin color." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Huib de Ridder. SPIE, 2015. http://dx.doi.org/10.1117/12.2077854.

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Srimaharaj, Wanus, Sirichai Hemrungrote, and Roungsan Chaisricharoen. "Cloud service for detection of human skin color." In 2015 15th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2015. http://dx.doi.org/10.1109/iscit.2015.7458294.

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Yasser, Hassan, Fatma Mohammed, Maram Mahir, and Alwaleed Abdelrahman. "Human skin color code recognition: A case study." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6634028.

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Mitsui, Masanori, Yuri Murakami, Takashi Obi, Masahiro Yamaguchi, and Nagaaki Ohyama. "Color enhancement in multispectral image of human skin." In Biomedical Optics 2003, edited by Richard M. Levenson, Gregory H. Bearman, and Anita Mahadevan-Jansen. SPIE, 2003. http://dx.doi.org/10.1117/12.477944.

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Ohya, Yuri, Takashi Obi, Masahiro Yamaguchi, Nagaaki Ohyama, and Yasuhiro Komiya. "Natural color reproduction of human skin for telemedicine." In Medical Imaging '98, edited by Yongmin Kim and Seong K. Mun. SPIE, 1998. http://dx.doi.org/10.1117/12.312499.

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Sekar, Sanathana Konugolu Venkata, Claudia Nunzia Guadagno, and Stefan Andersson-Engels. "Can LIDARs address skin color bias?" In Clinical and Translational Biophotonics. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/translational.2024.js4a.53.

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Abstract:
We evaluated fundamental properties of time-domain near-infrared spectroscopy to overcome color bias in NIRS devices. The Monte Carlo simulations, phantoms, and preliminary in vivo pseudo phantom-human measurements demonstrate how the effect of the superficial layer (skin color layer) can be eliminated to achieve bias-free sensing.
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Reports on the topic "Human skin color"

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Salardi, Paola, and Hugo R. Ñopo. Gender and Racial Wage Gaps in Brazil 1996-2006: Evidence Using a Matching Comparisons Approach. Inter-American Development Bank, May 2009. http://dx.doi.org/10.18235/0010913.

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This paper explores the evolution of Brazilian wage gaps by gender and skin color over a decade (1996-2006), using the matching comparison methodology developed by Ñopo (2008). In Brazil, racial wage gaps are more pronounced than those found along the gender divide, although both noticeably decreased over the course of the last decade. The decomposition results show that differences in observable characteristics play a crucial role in explaining wage gaps. While in the case of racial wage gaps, observable human capital characteristics account for most of the observed wage gaps, the observed gender wage gaps have the opposite sign than what the differences in human capital characteristics would predict. In both cases the role of education is prominent.
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