• Proceedings of the Korea Society of Costume Conference
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• 2003.10a
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• pp.39-39
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• 2003

The currently used personal color analysis is mostly based on Westerners' skin colors. As a result, the suggested colors are often not suitable for Korean people's skin colors. Accordingly, the purpose of this study is to develop 'Personal Color Design System(PCDS)' that can suggest fashionable colors suitable for Korean skin colors and personal color types. For this, the system was verified by customers testing and questionnaires, while the system modification and complementary measures were conducted.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.531-535
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• 1999

Automatic human face detection in a complex background is one of the difficult problems. In this paper, we propose an effective and robust automatic face detection approach that can locate the face region in natural scene images when the system is used as a pre-processor of a face recognition system . We use two natural and powerful visual cues, the skin color and the eyes. In the first step of the proposed system, the method based on the human skin color space by selecting flesh tone regions using normalized r-g space in color images. In the next step, we extract eye features by calculating moments and using geometrical face model. Experimental results demonstrate that the approach can efficiently detect human faces and satisfactory deal with the problems caused by bad lighting condition, skew face orientation.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.540-543
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• 1999

We propose a face tracking algorithm using skin-color based segmentation and a robust Hausdorff distance. First, we present L＊a＊b＊ color model and face segmentation algorithm. A face is segmented from the first frame of input video sequences using skin-color map. Then, we obtain an initial face model with Laplacian operator. For tracking, a robust Hausdorff distance is computed and the best possible displacement t. is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.217-220
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• 2001

The detection of face in color images is important for many multimedia applications. It is the first step for face recognition and ran be used for classifying specific shots. In this paper describes a new method to detect faces in color images based on the skin color and hair color. In the first step of the processing, regions of the human skin color and head color are extracted and those regions are found by their color information. Then we converted binary scale from the image. Then we are connected regions in a binary image by label. In the next step we are found regions of interesting by their region information and some conditions.

• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• Journal of Fashion Business
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• v.16 no.5
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• pp.106-113
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• 2012

This study intend to research color preferences about innerwear(specially brassiere) and draw a comparison of color preference's differences among the Korea, China and Hong Kong. The subjects of study are female students of universities in Korea, China and Hong Kong. The data analysis was done with the statistical treatment in SPSS 14.0, and the results are as follows. Female students of universities in Korea, China and Hong Kong are distinguished from wearing color and preference color of innerwear. Korea and China female students prefer skin color to the other color but most of Hong Kong female students prefer black color. In case of red color, Korea female students don't select a red color as a preference color but even if some students select a red color, China and Hong Kong females prefer a red color. The traditional color opinions of Korea, China and Hong Kong are the same as a Yin-Yang School. But these days they have different color opinions because of cultural, political and ideological elements. Korea females like skin and white colors because these colors don't appear on the outwear surface. Korean have an inclination toward conservatism and use the color according to ideological and deceptive orders of the Confucianism. Hong Kong have a different color preference from China because they have chances of receiving the other cultures for example United Kingdom, Japan and so on.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.127-130
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• 2007

Skin detection research is important role in the 3G of mobile phone for video telephony and security system by using face recognition. We propose skin detection algorithm as preprocessing to the face recognition, and use YCbCr color space. In existing skin detection algorithm using CbCr, skin colors that is brightened by camera flash or sunlight at outdoor in images doesn't acknowledged the skin region. In order to detect skin region accuracy into any circumstance, this paper proposes 3-channel skin detection algorithm.

• Journal of the Korean Society of Costume
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• v.55 no.6 s.96
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• pp.1-14
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• 2005

The purpose of this study is to examine the makeup color range for developing the new makeup product line of domestic cosmetic brand, and suggest the special color stories and color palettes appropriate to characteristics of brand image For this study, personal color images are analyzed and classified through the literature survey of Korean womens' skin rotor like korean PCDS and japanese PCS. Also, a total of 3879 colors were selected from the 18 cosmetic brands and were analyzed by hue/tone color system. Based on the color analysis, the color range for makeup products are determined, and the typical colors of 4 personal color images are suggested. As a results, personal color images are classified into 4 types: DEW of p, lt, b tone; FLASH of s, v tone; MIST of ltg, g, sf, d tone; TERRA of dk, dkg, dp tone. we developed the color stories through five senses and lifestyles based on the consumers' emotion, and the rotor palettes suitable to each type of personal color images are developed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1048-1052
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• 2004

In this work, we have investigated a role of linking parameter in Pulse-Coupled Neural Network(PCNN) which is suggested to explain the synchronous activities among neurons in the cat cortex. Then we have found a method to determine the linking parameter for a satisfactory face detection performance in a given color image. Face detection algorithm which uses the color information is independent on pose, size and obstruction of a face. But the use of color information encounters some problems arising from skin-tone color in the background, intensity variation within faces, and presence of random noise and so on. Depending on these conditions, PCNN's linking parameters should be selected an appropriate values. First we obtained the mean and variance of the skin-tone colors by experiments. Then, we introduced a preprocess that the pixel with a mean value of skin-tone colors has the highest level value (255) and the other pixels have values between 0 and 255 according to normal distribution with a variance. This preprocessing leads to an easy decision of the linking parameter of the Pulse-Coupled Neural Network. Through experiments, it is verified that the proposed method can improve the face detection performance compared to the existing methods.