• Fashion & Textile Research Journal
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• v.11 no.1
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• pp.103-111
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• 2009

This study is to help make-up and coordination for image-making after analysis of facial color and shape of elderly women. The data was analyzed 55-75 years old 212 elderly women's face color and pictures by means of SPSS 12.0 statistics package. On the basis of the colorimetric data on face by Minolta CM2500D, this research considered the analysis of facial color, patternization of facial color and its analysis by age group; for the analysis of facial shape, this research patternized facial shape and analyzed its characteristic using both contour-based facial shape analysis and Kamata facial shape analysis. As for facial color, it was found that the lower age bracket has bright and reddish face, looking fine, while the higher age bracket has a conspicuously yellowish face, looking bad. The community of facial color is classified as 3 types and it was found out that the facial color of the subjects belonging to Type 3, whose L value is the largest, looked the brightest; the face of the subjects belonging to Type 2, whose a value is the largest, was much tinged with red, and the face of the subjects belonging to Type 1, whose b value is the largest were tinged with yellow. According to the analysis of facial shape, there appeared oval & long forms in the classification by contour, while there appeared a lot of downward-directed power and inner-directed power in the classification by Kamata, which is believed to reflect the phenomenon that their chin line becomes roundish and the facial length also tend to be longer due to aging.

• The Research Journal of the Costume Culture
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• v.20 no.2
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• pp.144-153
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• 2012

This study performed a simultaneous sensory evaluation and color measurement, targeting 136 female university students who live in the Dae-Jeon region. the study measured participants'facial coloration under the condition of available light between 11 AM and 3 PM from Spring (May) to Autumn (October) in 2009. For statistical analysis, descriptive statistics, a member variate analysis, and discriminant analysis were executed using SPSS version 18.0 of the statistics program. The results of this study are as follows. First, as a result of the sensory evaluation, the blue undertone well matched to face type was dominantly distributed among the female university student participants. Second, the forehead showed a type of yellowish coloration and was relatively dark to cheeks. However the cheek displayed a reddish coloration and was relatively bright compared to the forehead from an evaluation of a cheek and forehead color measurement. Third, due to the investigation the of facial coloration variable, a yellowish and reddish chromaticity on the cheek were evident as a variable of facial coloration, which has an influence on the classification of the types of facial color. As a result of the induced discriminant through these two color variables, the yellowish chromaticity appeared as a color variable to have a greater influence than the reddish chromaticity on the cheek.

• Journal of Pharmacopuncture
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• v.20 no.2
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• pp.100-106
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• 2017

Objectives: This study aimed to assess a Qi Blood Yin Yang evaluation method systematically and objectively and to identify the correlation between the Qi Blood Yin Yang deficiency pattern (QBYYDP) and facial color. Methods: Thirty-seven participants (17 males, 20 females) were enrolled in this study. Twenty-four (10 males, 14 females) had ages from 40 to over 60, and 13 (7 males and 6 females) were in their twenties. After sufficient rest, facial images were taken with a camera. Based on the results from a questionnaire survey, we divided the participants into five groups: the normal and the Qi-, Blood-, Yin-, and Yang-deficient groups, after which the relationships between the L, 'a', and 'b' values in the Lab color system and the characteristics of the participants in each of the deficient groups were elucidated using a facial color analysis program. Results: The color analysis for Qi-deficient (QD) participants revealed that the L value was fairly decreased in comparison with the normal participants, but the 'a' and 'b' values were almost the same. A comparison between the normal and the Yang-deficient (YaD) groups revealed that the L values were somewhat lower compared to the normal group, but the 'a' and 'b' values were not statistically different. For the Yin-deficient (YiD) group, the L value was slightly lower compared to the normal group, but the 'a' and 'b' values were almost the same and the R values were slightly increased. For the Blood-deficient (BD) group, the L values were slightly increased compared to the normal group, but the 'a' and 'b' values were decreased slightly. Conclusion: This study obtained objective, reliable data for judging the QBYYDP by using facial images and a color analysis program. However, further study with at least 10 or more subjects in each of the deficient groups is necessary to confirm our findings.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.6 s.154
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• pp.971-980
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• 2006

The colors of apparel have a close relationship with the facial color types of consumers. To extract the favorable colors that flatter to consumer's facial color types, the facial colors of Korean females were analyzed. With color meter JX-777, 2 points of face were measured and classified into 3 clusters that had similar hue, value and chroma. Other new 10 college girls were measured and 3 subject among them were selected by the criteria that choose new subjects who have the classified facial color types. 175 respondents answered the degree of becomingness of color samples on three subjects. Data were analyzed by K-means cluster analysis, ANOVA and Duncan multiple range test using SPSS Win. 12. Findings were as follows: 1) 324 subjects who had YR facial colors were classified into 3 facial color groups. The average facial color Type 1 was 4.82YR 6.47/3.70 and composed 48.88% among total observations. Type 2 was 5.99YR 6.12/4.12 and 30.25%. Type 3 was 5.15YR 7.07/4.97 and 20.99% respectively. 2) Favorable colors for Type 1 were 18 colors that belonged to 'a' group from among colors that were divided into a, b, c group by Duncan post hoc test. 3) Type 2 showed that this type had many unfavorable colors. Unfavorable colors were 18 colors that belonged to 'c' by Duncan test. 4) Type 3 showed that black is the most favorable color and 18 colors were at middle level, which belonged to 'b' from among 18 colors that were divided into a, b, and c by Duncan test.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.2 s.150
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• pp.316-325
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• 2006

The colors of apparel are getting more important to give the differentiated character on fiber and fabrics. This study was to extract the favorable colors that become to facial color types. Research was carried out to classify the facial colors into several similar facial color groups. With JX-777, 2 points of face: forehead and cheek, were measured and classified into 3 facial color types. Sample size was 418 Korean adult males and other 15 of new males subjects. New chosen 3 subjects who had the classified facial color types, wore silver gown and black hat on his head to minimize the interaction of the clothe color an hair. The 40 standardized color samples were used to extract the favorable colors. 187 respondents answered the degree of becomingness of color samples on 3 facial color types. Data were analyzed by K-means cluster analysis, ANOVA and Duncan multiple range test using SPSS Win. 12. Findings were as follows: 1. 418 subjects who had YR colors were classified into 3 kinds of facial color groups. Type 1 was 4.59YR 5.89/5.12, Type 2 was 5.61 YR 5.41/4.79 and Type 3 was 4.38YR 6.49/4.89 respectively. 2. Favorable colors for Type 1 were 2 colors that belonged to ' a ' group from among colors that were divided into a, b, c group and 18 colors that belonged to ' a ' group from among colors that were divided into a, b group by Duncan post hoc test. 3. Type 2 showed that this type had many unfavorable colors. Unfavorable colors were 16 colors that belonged to ' c ' by Duncan test. 5. Favorable colors for Type 3 were 14 colors that belonged to ' a ' from among colors that were divided into a, b, c and 16 colors that belonged to ' a ' from among colors that were divided into a, b by Duncan test.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.329-339
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• 2007

In this paper we present a facial region detection algorithm for real-time image with complex background and various illumination using spatial and temporal methods. For Detecting Human region It used summation of Edge-Difference Image between continuous image sequences. Then, Detected facial candidate region is vertically divided two objected. Non facial region is reduced using Analysis of Major Color Component. Non facial region has not available Major Color Component. And then, Background is reduced using boundary information. Finally, The Facial region is detected through horizontal, vertical projection of Images. The experiments show that the proposed algorithm can detect robustly facial region with complex background various illumination images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2671-2677
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• 2014

These days, there is increasing those who like spicy food, people release stress by eating spicy food. But, when you eat spicy food, there is a difference but, we visually can be found red facial color to change. In this paper, when you eat spicy food, we carried out experiment which comparison and analysis color change of facial area depending on Sasang constitutional type. To this end, we organized test subject group by Sasang constitutional type according to survey result for Sasang constitutional type. And then we carried out interrelationship analysis between spicy food and facial color depending on facial color to apply Lab color system based on facial image which is before and after eat a hot spicy pepper.

• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.610-619
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• 2015

*In this paper, we propose a real-time face tracking system using an adaptive face detector and a tracking algorithm. An image is divided into the regions of background and face candidate by a real-time updated skin color identifying system in order to accurately detect facial features. The facial characteristics are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted by Principal Component Analysis (PCA), and the interpreted principal components are processed by Support Vector Machine (SVM) that classifies into facial and non-facial areas. The movement of the face is traced by Kalman filter and Mean shift, which use the static information of the detected faces and the differences between previous and current frames. The proposed system identifies the initial skin color and updates it through a real-time color detecting system. A similar background color can be removed by updating the skin color. Also, the performance increases up to 20% when the background color is reduced in comparison to extracting features from the entire region. The increased detection rate and speed are acquired by the usage of Kalman filter and Mean shift.