• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.59-66
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• 2011

In this paper, we propose a face detection method based on color constancy and geometrical analysis. With the problem about the various colors of skin under scene illuminant, a color constancy method is applied to input images and geometrical analysis is used to detect face regions. At first, the candidates of face or hair are extracted from the image that a color constancy method is applied to, and are classified by some geometrical criterions. And then, face candidates which have some intersectional regions whose total is over a certain size, with hair candidates are selected as faces. Caltech Face DB was used to compare the performance of our method. Also, performance about scene illuminant was evaluated by images which have some illumination effects. The experiment results show that the proposed face detection method was applicable to various facial images because of high true-positive and low false-negative ration.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.249-254
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• 2013

In the area of computer vision, face recognition is being intensively researched. It is generally known that before a face is recognized it must be localized. Skin-color information is an important feature to segment skin-color regions. To extract skin-color regions the skin-color model based on multi-layer perceptron has been proposed. Extracted regions are analyzed to emphasize ellipsoidal regions. The results from this study show good accuracy for our vehicle driver face detection system.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

• Journal of IKEEE
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• v.6 no.2 s.11
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• pp.152-160
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• 2002

This paper is the realization of the face recognition system for multimedia application. This system is focused on the design concerning the improvement of recognition rate and the reduction of processing time for face recognition. The non-modificated application of typical RGB color system enables the reduction of time required for color system transform. The neural network and the application of algorithm using face characteristic improves the recognition rate. After mosaicking an image, a face-color block has been selected through the color analysis of mosaic block. The characteristic of the face removes the mis-checked face-color candidate block. Finally, from the face color block, four special values are obtained. These values are processed to the neural network using the back propagation algorithm. The output values are the touchstone to decide the genuineness of face field. The realized system showed 90% of face recognition rate with less than 0.1 second of processing time. This result can be understood as sufficient processing time and recognition rate to find out the face block for multimedia application in dynamic image.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.8
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• pp.1287-1296
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• 1997

The purpose of the study is to make experimental whether three-dimensional body (especially face) has illusion based on theoretical background of form dimensions and color bright among precedent multi-form illusion, using the function of computer simulation. To investigate illusion that factors of clothing design(line, color, material) effect face, as the following is tried to solve giving change to neckline, collar, scarf which is believed to influence near face. How to make experiment as follows watching in order 13 scenes of a pair of with basic design and experiment design. Then the data were subjected to analysis of variance and Duncan's multiple range test. The result of this studying as follows, 1. Face looks larger in complex neckline than simple neckline. The larger collar is the larger face looks. 2. In white jacket, illusion(the lower luminosity of scarf color gets, the brighter face brightness gets) is shown. In black jacket, also illusion(the higher luminosity of scarf color gets, the darker face brightness gets) is shown. 3. In experiment on hardness and softness of face impression according to the material of collar, collar of knit and fur gives us assimilation illusion bring softer impression of face.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.542-548
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• 2006

In this paper, we propose a face region detection/verification method using wavelet packet analysis and structural statistic for frontal human color image. The method extracts skin color lesions from input images, first. and then applies spatial restrictive conditions to the region, and determines whether the region is face candidate region or not. In second step, we find eye region in the face candidate region using structural statistic for standard korean faces. And in last step, the face region is verified via wavelet packet analysis if the face torture were satisfied to normal texture conditions.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.959-967
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• 2005

The color of apparels has the interaction of the face skin colors of the wearers. This study was carried out to classify the face skin colors of Korean males into several similar face skin colors in order to extract favorable colors which flatter to their face skin colors. The criterion that select the new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured quantitatively and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 418 Korean males and other 15 of new males subjects. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 418 subjects who have YR colors were clustered into 3 kinds of face skin color groups. 2. Discriminant variables of face skin colors was 4 variables : L value of forehead, v value of cheek, c value of forehead, and b value of cheek from standardized canonical discriminant function coefficient 1 and c value of forehead, L value of forehead, b value of cheek. and L value of cheek from standardized canonical discriminant function coefficient 2. 3. Hit ratio of type 1 was $92.3\%$, of type 2 was $96.5\%$ and of type 3 was $92.6\%$ by the canonical discriminant function of 4 variables. 4. The canonical discriminant function equation 1 and 2 were calculated with the unstandardized canonical discriminant function coefficient and constant, the cutting score, and range of the score were computed. 5. The criterion that select the new subjects who have the classified face skin colors was decided.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.25-31
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• 2005

In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.

• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.527-534
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• 2005

The purpose of this research is to study face images according to color of make-up was made by computer graphic simulation. The various facial images can be helpful for choosing suitable make-up color planning. In order to find out the differences of face images by make-up color, three different foundations and seven eye-shadows, six lips were applied on the round face model. Make-up Image Scale was used the scale of seven point modified the S-D method. Data were analyzed by Varimax perpendicular rotation method, Duncan's Multiple Range Test, Three-way ANOVA. As the result of make-up image perception analysis, a factor structure was divided into mildness, modernness, elegance, unique. The factor of mildness, modernness, unique affected on the foundation color. Foundation color was found out to be influential variable to distinguish color perception abilities. Also, the foundation, eye-shadow, lip color were influenced interactively on the perception of elegance factor. Pink color was important color, influenced on the mildness factor. Gray and purple color were influenced on the modernness factor. Mildness factor was perceived as the most bright foundation but unique factor was perceived as the most dark foundation. Then, the foundation, eye-shadow, lip color were influenced interactively on the perception of facial images. The results can be effectively applied to today's marketing and color design management which is focused on the product's emotional image in customer's mind.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.42-48
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• 2000

In this paper, we propose the face image recognition algorithm using skin color information, face region features such as eye, nose, and mouse, etc., and geometrical features of chin line. In the proposed algorithm, we used the intensity as well as skin color information in the HSI color coordinate which is similar to human eye system. The experimental results of proposed method shows improved extraction quality of face and provides adaptive extraction methods for the races. And also, we used chin line information as well as geometrical features of face such as eye, nose, mouse information for the improvement of face recognition quality, Experimental results shows the more improved recognition as well as extraction quality than conventional methods.