• 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%).

• 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 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 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.

• 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 Costume
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• v.44
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• pp.104-116
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• 1999

This study was intended to make an inquiry into the effect of lines, shapes and colors (achromatic colors) as apparel design elements on the perceptions of facial size, brightness and shape through an empirical research based on the theories of visual perception and optical illusion that have been studied in Gestalt Psychology, which becomes the basis of apparel design principles. It was shown that the facial size was influenced by the collar size, the degree of neckline cut, and the thickness of the trimming line. The white scarf with the white jacket made the face look darkest while the black scarf with the black jacket made the face look brightest. When the neckline had the characteristic of the line contrasted with the facial shape, the oval-shaped face was perceived to be the most ideal. When the facial shape and the neckline shape were similar, the facial shape was perceived to be more highlighted.

• 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 korean journal of orthodontics
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• v.13 no.1
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• pp.105-114
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• 1983

the author studied the corelativity between the head and face and the maxillary arch in Korean. This study was undertaker in 336 persons at age from 9 to 19 years who had normal occlusion by means of angle's classification. The following results were obtained. 1. The corelative coefficient between the Height of Head and Face (H.H.F.) and the Arch Length (A.L.) was 0.203-0.543, 2. The corelative coefficient between the Bizygomatic width (Z.W.) and the Bicanine width (C-C) was 0.203-0.543. 3. The corelative coefficient between the Bizygomatic width (Z.W.) and the Bimolar width (M-M) was 0.206-0.600. 4. The corelative coefficient between the Face shape (Index a) and Maxillaxy arch shape (In-dex c) was 0.232-0.404. 5. The corelative coefficient between the Face shape (Index a) and Maxillary arch shape (Index d) was 0.221-0.401. 6. There was no corelativity between the Anterior-posterior width of head (A.P.W.) and Arch Length A.L.), Head shape (Index b) and Maxillary arch shape (Index c, Index d).

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.32-38
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• 2006

During stereolithography processes, the shape errors such as curl distortion and distortion of side face are generated due to the shrinkage of liquid resins. In this study, the effects of dimension of part and structure of supports on the shape error are examined. Cubic specimens which have different thicknesses are manufactured and their deformations are measured with CMM. Thicker part generates smaller curl distortion of top face and larger of bottom face. Also thicker part generates larger distortion of side face until part thickness increases to about 20mm. Larger stiffness of supports which is obtained by shorter spacing of the supports and line type contact instead of point type contact generates smaller shape error of the part.

• Advanced Composite Materials
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• v.17 no.1
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• pp.41-56
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• 2008

In this research, optical fiber sensors and shape memory alloys (SMA) were incorporated into sandwich panels for development of a smart honeycomb sandwich structure with damage detection and shape recovery functions. First, small-diameter fiber Bragg grating (FBG) sensors were embedded in the adhesive layer between a CFRP face-sheet and an aluminum honeycomb core. From the change in the reflection spectrum of the FBG sensors, the debonding between the face-sheet and the core and the deformation of the face-sheet due to impact loading could be well detected. Then, the authors developed the SMA honeycomb core and bonded CFRP face-sheets to the core. When an impact load was applied to the panel, the cell walls of the core were buckled and the face-sheet was bent. However, after the panel was heated over the reverse transformation finish temperature of the SMA, the core buckling disappeared and the deflection of the face-sheet was relieved. Hence the bending stiffness of the panel could be recovered.