• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.1-12
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• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Journal of the Korean Graphic Arts Communication Society
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• v.14 no.2
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• pp.115-128
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• 1996

In this paper, we describe the Color Correction and Preferred Skin Color Reproduction in a ink-jet color printer. The fundamental color correction that converts RGB densities into GMY densities has been ordinarily used. This method can reduce the hue error, but color difference between the preferred skin colors and hardcopy skin colors are large. We have been able to reduce color differences between original skin color and hardcopy skin color by transforming hardcopy skin colors` coordinates into Preferred Skin Colors` coordinates. Experimental results show that the described method is useful and valid for the skin color reproduction in a digital color printer.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.231-239
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• 2005

This paper describes a new algorithm for pixel-based skin color detection to differentiate human form in color images by the ratio of R to H. In order to detect skin color efficiently, we examine the distribution of the R, G and B color elements combining to constitute the skin color in various color images. It shows that R is located in a narrower area than G and B on the RGB color space. And skin color is more related to R than G and B. Meanwhile, when the color image is transformed to the HSI color space, the S is variously changed in accordance with skin colors. The I is changed in accordance with the quantity and angle of light. But the H is less influenced by other conditions except for color. On the basis of the aforementioned study, we propose that the threshold for skin color detection is decided by the ratio of R to H. The proposed method narrows down the range of threshold, detects more skin color and reduces mis-detection of skin color in comparison to detection by R or H. In experimentation. it shows that the proposed algorithm overcomes changes of brightness and color to detect skin color in color images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1809-1818
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• 2010

In order to detect the skin color area from input images, many prior researches have divided an image into the pixels having a skin color and the other pixels. In a still image or videos, it is very difficult to exactly extract the skin pixels because lighting condition and makeup generate a various variations of skin color. In this thesis, we propose a method that improves its performance using hierarchical merging of 3D skin color model and context informations for the images having various difficulties. We first make 3D color histogram distributions using skin color pixels from many YCbCr color images and then divide the color space into 3 layers including skin color region(Skin), non-skin color region(Non-skin), skin color candidate region (Skinness). When we segment the skin color region from an image, skin color pixel and non-skin color pixels are determined to skin region and non-skin region respectively. If a pixel is belong to Skinness color region, the pixels are divided into skin region or non-skin region according to the context information of its neighbors. Our proposed method can help to efficiently segment the skin color regions from images having many distorted skin colors and similar skin colors.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.38-45
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• 1997

Skin color reappearance problem in color processing system is necessary to transform a specific color sand improve color reappearance quality as a reference color. The skin color has been situated as an important memory color not only in our lives but also in color application systems such as TV. Thus, skin color reappearance problem is more important than other color processing problem. In this paper, we propose a skin color reappearance algorithm for color enhancement in TV which use phase detector to detect the skin colors at real-time from 3.58 MHz color burst signal and color signal, comparators to discriminate the types of skin color, and micom to reprodece standard skin colors for races.

• The Journal of Advanced Prosthodontics
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• v.10 no.6
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• pp.422-429
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• 2018

PURPOSE. Accurate color matching of maxillofacial prostheses to skin is important for esthetics. A computerized color matching system specific to human skin has recently been developed. The purpose of this study was to evaluate the accuracy in color and translucency matching of the computerized color matching system across different skin colors. MATERIALS AND METHODS. The silicone was colored to simulate 28 different skin colors (n=5) to serve as "target skin colors". Using a spectrocolorometer (e-skin), color codes were determined for "replicate skin color" fabrication. CIELAB Delta-E between target skin color-replicate skin color pairs and translucency parameter were calculated. CIELAB Delta-E values were compared with one-way ANOVA and Tukey multiple-comparison. The agreement between $L^*$, $a^*$, $b^*$ and translucency parameter of target skin colors and replicate skin color were calculated by a two-way mixed average measures intraclass correlation coefficient. Translucency parameter of target skin color- replicate skin color pairs were compared with Paired t-test (${\alpha}=.05$). RESULTS. The mean CIELAB Delta-E value was 3.83 and significant differences were found among colors. The intraclass correlation coefficient showed excellent reliability for $L^*$, $a^*$, $b^*$ and good reliability for translucency parameter (P<.001). The mean translucency parameter of replicate skin colors was significantly higher than that of translucency parameter. CONCLUSION. The computerized color matching system specific to human skin was found to be reliable in terms of color and translucency between target skin colors and replicate skin color.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.432-435
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• 2004

The skin color of a human being is the important memory color influencing image quality for color display. Therefore, in this paper, the preferred skin color axis is defined on HSV color space by analyzing some previous research, and the preferred skin color reproduction algorithm is performed by rotating the center axis of skin distribution of an input image to the preferred skin color axis.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.283-299
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• 2014

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.10-15
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• 2012

In a color image, people and especially facial patterns are important and interesting visual objects. Thus, effective skin color reproduction is essential, as skin color is a key memory color in color application systems. Previous studies suggested skin color reproduction by mapping only to the center value of preferred skin region. However, it is not suitable to determine one preference color because preference color from the observer's preference test is not dominant. In this paper, skin color reproduction using multiple preferred skin colors for each race is proposed. The proposed method first defines multiple preferred skin colors for each race according to their luminance level. After that, skin region is detected in an image. The race is then selected by calculating distance between average chromaticity of detected region and that of each racial skin from a database to assign preferred skin color for each race. Next, each corresponding preferred skin color is determined for each selected race. Finally, input skin color is proportionally mapped toward preferred skin color according to the difference between the input skin color and the preferred skin color for a smoothly reproduced skin color. In the experimental results, the proposed method gives better color correction on the objective and subjective evaluation than the previous methods.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.60-67
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• 2008

Skin color is a very important information for an automatic face recognition. In this paper, we proposed a skin region extraction method using color information and skin color model. We use the adaptive lighting compensation technique for improved performance of skin region extraction. Also, using an preprocessing filter, normally large areas of easily distinct non skin pixels, are eliminated from further processing. And we use the modified ST color space, where undesired effects are reduced and the skin color distribution fits better than others color space. Experimental results show that the proposed method has better performance than the conventional methods, and reduces processing time by $35{\sim}40%$ on average.