• The Research Journal of the Costume Culture
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• v.8 no.1
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• pp.133-144
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• 2000

The purpose of this study was to classify women's face types by visual distinction and to analyze the measurement of face types. A survey was conducted by subjects of 167 women's college students in Kwangju City and Chonnam area. Data were analyzed by Frequencies, Mean, one way ANOVA and Ducan's Multiple Range Test. The major results were as followed ; ·Women's face types were classified by 7 types and there were oblong shape(28.3%), egg shape(25.7%), round shape(23.9%), square shape(12.4%), inverted triangle shape(5.3%), diamond shape(3.5%), triangle shape(0.8%) in the subjects. ·From the measurements of the women's face, index of face length to face breadth was 1.38, it means that the index was different from the other refferences. And the lower face length was longer than the upper and the middle face lengths. ·Differences From those measurements like forehead breadth, face length/bizigion breath(p〈.001), bizigion breadth, bignathion slopper, stature(p〈.01) and trichion breadth, tragion-menton length(p〈.05) were significant in the classified face types.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.11
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• pp.1470-1481
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• 2004

The purpose of this study was to research on facial shape to suggest a quantified data for the domestic apparel and beauty industry. Conducted a measurement research of 278 female college students, We took the photographs of front view and lateral view of the subjects by digital camera and obtained the 69 measurements through the facial measurement program. 264 ,subjects' measurement data were analyzed by various statistical methods such as descriptive analysis, factor analysis and cluster analysis. Using the 69 measurement items,4 factors were selected as key factors for the factor analysis of facial shape, the factors are: \circled1 Front face height \circled2 Side face radial length \circled3 Front face breadth \circled4 Ear height and Gnathion radial length. We categorized the facial shape into four types by cluster analysis. Type 4 is the most common facial shape in female college students: \circled1 Type 1: Round face \circled2 Type 2: Oval face \circled3 Type 3: Square face \circled4 Type 4: Heart shaped face According to the facial shape analysis, facial shape of female college students are consisting of Heart shaped face(34.8%), Round face(29.2%), Square face(23.5%), oval face(12.5%).

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• Journal of Fashion Business
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• v.8 no.1
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• pp.76-90
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• 2004

The purpose of this study was to classify women's face types and to analyze the measurement of face types. For study, 180 adult women(aged between 20 and 29) in Pusan and Ulsan area was sampled to be measured for facial types. Data were analyzed by Frequencies, Means, Duncan's Multiple Range Test, Distinction analysis. The major results were as followed. Women's face types were classified by 6 types and there were round shape(29.4%), oblong shape(18.9%), inverted triangle shape(16.1%), square shape(13.9%), egg shape(11.7%), diamond shape(10.0%) in the subject. Phyiognomic facial height was 182.38mm, the upper face length was 59.82mm, the middle face length 60.82mm, the lower face length 61.76mm, and the index of face length to face breadth was 1.35. The face width was 134.90mm, interocular distance 34.75mm, the nose width 33.93mm, and mouth width was 43.87mm. And also, differences from those measurements like forehead breadth, face length/bizygion breadth, forehead slopper, bigonion breadth, bignathion breadth, bignathion slopper.

• Journal of the Korean Society of Costume
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• v.57 no.5 s.114
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• pp.151-165
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• 2007

Appearance matters in society today. Women want to feel good and look their best. They do make-up, wear garment and accessory for their good looking. Doing make-up, we have to know how we are look and to consider face shape. But it is difficult to recognize face shape. Because there is no standard face shape of adult women of quantitative analysis. The purpose of this study was to offer standard face shape of adult women in Korea. Furthermore, the study was to determine and differentiate face shape of each age group to set the basic data for the Korean beauty industry. In this study, photographs of 600 Korean women, age between $20{\sim}50's$, were indirectly measured in Venus face2D program. The measurements were analyzed by statistical methods. As a result of basic statistical data analysis, the average lengths of face were 196mm, lengths of forehead-hairline between eyebrows were 62mm, lengths of eyebrow between noses were 68mm, length of nose between chin were 66mm, and width of face were 150mm. By comparing to each age group's face using ANOVA, the statistically noticeable differences were found in measurements.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.6
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• pp.938-948
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• 2009

The purpose of this study was to offer typical facial shapes Korean women in their 20's to 50's. We used facial photographs of 600 Korean women obtained from $2003\sim2004$ Size Korea Project and we measured these photographs indirectly in this study by utilizing the Venus face2D program. Total 62 measurements on the face were measured and analyzed by statistical methods. The results were as follows. First of all, the mean of face length was 196mm, top face length was 62.3mm, middle face length was 68.9mm, bottom face length was 66.5mm, mean of forehead width was 125.1mm. As based on those average sizes, we proposed a average facial size and shape of Korean women and a average facial size and shape of 20's, 30's, 40's and 50's in this study. When examined characteristic of 20's facial shape, it was recognized that the width of forehead was wider and the width of gnathion was smaller than other age groups. In the characteristic of 30's facial shape, the ratios of facial length, top of face, middle of face and bottom of face were balanced well, as comparing with other age groups. Overall, the values of facial measurement of 30's were similar to the averages of total women. In the facial shape of 40's, mean length and width of face each were the smallest among each age group. The eye shape of 40's was more drooped than the average eye shape and the protrusion of the zygomatic bone was significantly different. In case of the facial shape of 50's, it was similar to the facial shape of 40's, but mean lengths and widths of 50's face were slightly larger than the values of 40's. The eye shape of 50's was more drooped than average group and the eye length was the smallest among all age groups.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.212-227
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• 2008

Since 3D face can be rotated freely in 3D space and illumination effects can be modeled properly, 3D face modeling Is more precise and realistic in face pose, illumination, and expression than 2D face modeling. Thus, 3D modeling is necessitated much in face recognition, game, avatar, and etc. In this paper, we propose a 3D face modeling method based on 3D morphable shape modeling. The proposed 3D modeling method first constructs a 3D morphable shape model out of 3D face scan data obtained using a 3D scanner Next, the proposed method extracts and matches feature points of the face from 2D image sequence containing a face to be modeled, and then estimates 3D vertex coordinates of the feature points using a factorization based SfM technique. Then, the proposed method obtains a 3D shape model of the face to be modeled by fitting the 3D vertices to the constructed 3D morphable shape model. Also, the proposed method makes a cylindrical texture map using 2D face image sequence. Finally, the proposed method builds a 3D face model by rendering the 3D face shape model with the cylindrical texture map. Through building processes of 3D face model by the proposed method, it is shown that the proposed method is relatively easy, fast and precise than the previous 3D face model methods.

• Journal of Fashion Business
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• v.12 no.1
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• pp.164-172
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• 2008

A face is the place where individuals can first give their images visually. This chapter presents how 'Visual optical illusion' works in applying makeup and how to differentiate the direction, location and shape of Lip in the aspect of physiognomy. For this study, employed were five types of face shape produced by Photoshop program. The best-matched facial shape was examined through a questionnaire research after applying the optical illusion of Lip to the five types of face shape. The results were revealed to be identical to ones presented in make-up teaching materials. In conclusion, it was found that well-matched shape and size of Lip could make some changes in the facial impression, changing the face shape into oval shape. The facial line can be modified and supplemented by reshaping such facial parts as the Lip, producing well-balanced facial shape. Consequently, make-up was proved to be one of the methods which can be used to create social and psychological effect which can make a favorable facial impression and individuality, natural impression and image making depending on different purposes, taking advantage of optical illusion effect.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.109-116
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation for the identification of a face shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the face area through pre-processing using a face shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a face area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the face shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.