• 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.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1545-1548
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• 2003

This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral images. It utilizes both fluorescence and reflectance image information in hyperspectral images. A detection system that is built on this concept can increase detection rate and reduce processing time. Chicken carcasses are examined first using band ratio FCM information of fluorescence image and it results in candidate regions for skin tumor. Next classifier selects the real tumor spots using PCA components information of reflectance image from the candidate regions.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.111.1-111
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• 2002

System configuration 1. First is the image acquisition part 2. Second part is for creating the vector image and for processing the obtained facial image. This part is for finding the facial area from the skin color. To do this, we can first find the skin color area with the highest weight from eigenface that consists of eigenvector. And then, we can create the vector image of eigenface from the obtained facial area. 3. Third is recognition module portion.

• Journal of the Korean Graphic Arts Communication Society
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• v.16 no.3
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• pp.97-108
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• 1998

Spectral reflectance of the object should be measured to predict the color of object under various illuminants. The spectral reflectance can be represented in a multi-dimension space. Generally the information of inputed image by digital camera and color scanner is represented with 3-dimension color signals such as RGB. In other to predict the color of inputed image under any illuminant, we should be estimated spectral reflectance of the object. In this paper, we described the method to predict spectral reflectance by einenvector using the skin color of printed image, confirmed availability and propriety through experiment. we estimated spectral reflectance of skin color taken by RGB color signals and than reproduced skin color according to various illuminants on CRT.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.806-817
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• 2014

The extraction of lip region is essential to Lip Reading, which is a field of image processing to get some meaningful information by the analysis of lip movement from human face image. Many conventional methods to extract lip region are proposed. One is getting the position of lip by using geometric face structure. The other discriminates lip and skin regions by using color information only. The former is more complex than the latter, however it can analyze black and white image also. The latter is very simple compared to the former, however it is very difficult to discriminate lip and skin regions because of close similarity between these two regions. And also, the accuracy is relatively low compared to the former. Conventional analysis of color coordinate systems are mostly based on specific extraction scheme for lip regions rather than coordinate system itself. In this paper, the method for selection of effective color coordinate system and chromaticity transformation to discriminate these two lip and skin region are proposed.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.575-578
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• 1999

The principal features of a face are as follows : skin-tone, symmetry, and requisites such as shape of ellipse, eyes, nose, mouth. Also, faces have different size, various shape and position. In case of application of face recognition and detection without preprocessing, efficiency of the performance is decreased. In addition, face itself, complex background, image quality, etc. are included. Therefore, previous face recognition methods are implemented on the base of specific constraints of the face image. In this paper, we propose the efficient and automatic face detection algorithm for minimizing influence such as complex background, image quality, etc. This face detection technique consists of skin-tone, candidate face region and face region extractions.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.16 no.1
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• pp.18-29
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• 1990

In this report, the variation of skin surface between before treatment and after treatment was investigated by skin image analyzer. The 30 volunteers were applied cream and lotion on the forearm Inside for 4 weeks, and the measurement parameter of skirl surface was adopted peak, length, height, and width. The results were as follows ; 1. variation trends of parameters was showed that number of peaks were increased, but length, height and width were decreased. 2. The order of improvement for skin surface are as follow ; height > length > peak > width. 3. And also disclosed that the number of peak has a correlation with width, and length has a correlation with height.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.38-43
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• 2008

Skin color information is an important feature for face region detection in color images. This can detect face region using statistical skin color model who is created from skin color information. However, due to the including of different race of people's skin color points, this general statistical model is not accurate enough to detect each specific image as we expected. This paper proposes method to detect correctly face region in various color image that other complexion part is included. In this method set face candidate region applying complexion Gausian distribution based on YCbCr skin color model and applied mathematical morphology to remove noise part and part except face region in color image. And achieved correct face region detection because using Haar-like feature. This approach is capable to distinguish face region from extremely similar skin colors, such as neck skin color or am skin color. Experimental results show that our method can effectively improve face detection results.

• Journal of Fashion Business
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• v.14 no.1
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• pp.43-56
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• 2010

In the modern 21st century society, the personal image is considered to be very important. As a result, the importance of presenting one's personal image through personal color in fashion and beauty related fields are increasing, and is the most realistic and practical field of color. When the color of the wardrobe and the skin color are in disharmony, that disharmony becomes the source of the lines and wrinkles that appear on one's face, resulting in shades. The boundary that is created when the color of the wardrobe and the skin color are in disharmony, it works negatively on one's image. When color arrangements are close or similar (in harmony) or are in complementary color arrangements or in strong contrasting state (contrasting harmony), it is generally believed to be beautifully harmonious. Personal color assessment is finding colors, through systematic and scientific methods, that improve the personal image by reaching harmony with skin colors that each and every individual are uniquely born with. In this study, one was able to learn the improved visual effects of the face image through creating harmony with the wardrobe and color shade make up and complementary colors that were selected based on personal colors. The base make up, through using the contrasting effects of the complementary colors which represents the supplementing, correcting, and complementing of the face image by contrasting with complementary colors, brings positive changes through correcting the base skin color. It is believed that this study finds its importance in that the improved image that is created by the overall harmony of the wardrobe and body can be used as valuable data in marketing and new product development efforts in the related industries.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.139-142
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• 2001

This paper presents a highly fast and accurate facial region extraction method by using the skin-color-reference map and motion information. First, we construct the robust skin-color-reference map and eliminate the background in image by this map. Additionally, we use the motion information for accurate and fast detection of facial region in image sequences. Then we further apply region growing in the remaining areas with the aid of proposed criteria. The simulation results show the improvement in execution time and accurate detection.