• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• The International Journal of Costume Culture
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• v.6 no.2
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• pp.93-104
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• 2003

The purpose of this study was to investigate the relationship between women's face satisfaction and makeup satisfaction, to disclose the differences of makeup satisfaction according to demographic variables, and to examine how makeup satisfaction was influenced by face satisfaction and demographic variables. The subjects were 200 women over age 17 living in Seoul and its peripheral areas. The results of this study were as follows: Face satisfaction were drawn three factors. Factor 1 was face contour satisfaction, Factor 2 was skin satisfaction, and Factor 3 was lips and eyes satisfaction. There were significant positive relationship between factors of face satisfaction and makeup satisfaction. Also, the face contour satisfaction was in positive correlation with satisfaction of features, and the skin satisfaction was in positive correlation with that of features. There were significant positive correlations between makeup satisfaction and face shape, eyes, nose, lips, chin, and cheek bone satisfaction. Face satisfaction didn't show significant difference according to demographic variables, but makeup satisfaction showed significant difference according to age and occupation. Face satisfaction was influenced by the facial face, clarity of skin, elasticity of skin, skin color, and ages. The explanatory power of the 4 variables were 24.5%. Makeup satisfaction was influenced by lips and eyes satisfaction, ages, and skin care level. The explanatory power of the 3 variables were 13.3%.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• 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.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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.828-831
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• 2005

In this paper we propose a method to detect human faces from color image using pupil-template matching. Face detection is done by three stages. (i)separating skin regions from non-skin regions; (ii)generating a face regions by application of the best-fit ellipse; (iii)detecting face by pupil-template. Detecting skin regions is based on a skin color model. we generate a gray scale image from original image by the skin model. The gray scale image is segmented to separated skin regions from non-skin regions. Face region is generated by application of the best-fit ellipse is computed on the base of moments. Generated face regions are matched by pupil-template. And we detection face.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2331-2334
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• 2003

Generally, skin color information has been widely used at the face region extraction step of the face region recognition process. But many experimental results show that they are very sensitive to the given threshold range which is used to extract the face regions at the input image. In this paper, we propose a face region extraction algorithm based on an adaptive range decision for skin color. First we extract the pixels which are regarded as the candidate skin color pixels by using the given range for skin color extraction. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face region. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• 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.

• International Journal of Contents
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• v.9 no.3
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• pp.9-14
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• 2013

In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.