• The Journal of the Society of Korean Medicine Diagnostics
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• v.22 no.1
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• pp.19-31
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• 2018

For the purpose of the basic educated-establishment on the diagnostic methods of "facial color and shape which reflect human's spiritual essence and personality", we study on the diagnostic value and application of the human face. The study's domain is divided the form and color of human face. And the form and color of human face is respectively observed the diagnostic value and contents. The form of human face reflect plenty the information of the mankind, and the observation of the face is applied to the "Physiognomie" refering to the external features of humans. Therefore the diagnosis on the form of human face is the primary factor in the grouping of five-element human, the discrimination of the Sasang constitution, and the classification of Hyunsang type. The color of human face reflect the physical information of internal organs and the pathological change of disease, therefore we examine the region, character and grade of disease by the inspection of complexion including the changes of color and luster of the facial skin. The inspection on the color is also the primary factor in the grouping of five-element human, the classification of Hyunsang and the differentiation of syndromes. The value of the inspection of complexion including the changes of color and form of the face is widely known. In the future, we think, we need to study more about the theory of the diagnostic value and application of the human face.

• 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).

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.985-990
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• 2003

This paper presents the representation and its shape analysis of face by features based on surface curvature estimation and proposed rotation vector of the human face. Curvature-based surface features are well suited to use for experimenting the 3D human face segmentation. Human surfaces are exactly extracted and computed with parameters and rotated by using active surface mesh model. The estimated features were tested and segmented by reconstructing surfaces from the face surface and analytically computing Gaussian (K) and mean (H) curvatures without threshold.

• Journal of Sasang Constitutional Medicine
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• v.19 no.2
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• pp.30-39
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• 2007

1. Objectives The Face is an important standard for the classification of Sasang Constitution. We are developing 3D Automatic Face Recognition Apparatus(3D-AFRA) to analyse the facial characteristics. This apparatus show us 3D image and data of man's face and measure facial figure data. So we should examine the figure restoration error of 3D Automatic Fare Recognition Apparatus(3D-AFRA) in hardware Error Analysis. 2. Methods We scanned Face status by using 3D Automatic Face Recognition Apparatus(3D-AFRA). And also we scanned Face status by using laser scanner(vivid 9i). We compared facial shape data be restored by 3D Automatic Face Recognition Apparatus(3D-AFRA) with facial shape data that be restorated by 3D laser scanner. And we analysed the average error and the maximum error of two data. 3. Results and Conclusions In frontal face, the average error was 0.48mm. and the maximum error was 4.60mm. In whole face, the average error of was 0.99mm. And the maximum error was 6.64mm. In conclusion, We assessed that accuracy of 3D Automatic Face Recognition Apparatus(3D-AFRA) is considerably good.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.231-234
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• 2001

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.196-204
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• 2008

In this paper, we propose an efficient method to classify human facial impression using frontal face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. PCA is used to project the feature space to a low dimensional subspace. LDA produces well separated classes in a low dimensional subspace even under severe variation. This results in good discriminating power for classification. SVM is used to classify the data. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.603-614
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• 2022

The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.41-49
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• 2006

1. Objectives The Face is an important standard for the classification of Sasang Contitutions. We are developing 3D Face Automatic Recognition Apparatus to analyse the facial characteristics. So We should examine a shape demobilization error of 3D Face Automatic Recognition Apparatus. 2. Methods We compared facial shape data be demobilized by 3D Face Automatic Recognition Apparatus with facial shape data that be demobilized by 3D laser scanner. The subject was two korean men. And We analysed the average error and the maximum error of two data. In this process, We used one datum point(the peak of nose) and two datum line(vertical section and horizontal section). 3. Results and Conclusions In each this comparison, the average error of vertical section was 1.962574mm and 2.703814mm. and the maximum error of vertical section was 16.968249mm and 18.61464mm. the average error of horizontal section was 4.173203mm and 21.487479mm. and the maximum error of horizontal section was 3.571210mm and 17.13255mm. Also We complemented this apparatus a little and We reexamined a shape demobilization error of 3D Face Automatic Recognition Apparatus again. Accuracy of a shape demobilization was improved a little. From now on We complement accuracy of a shape demobilization in 3D Face Recognition Apparatus.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.7
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• pp.983-994
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• 2004

The aim of this study is to provide fundamental data on the development of ready-to-wear clothes appropriate for the body types of elderly women. The study was conducted targeting 318 elderly women over 60 years of age whose fields of action were colleges for the elderly, sports centers, or business sites in Seoul and the neighboring districts. A total of 44 features in the upper body were used for the anthropometric measurement and analysis using anthropometry and photometry. The results of the study are as follows: 1. Somatotypes were classified into three types according to a cluster analysis using height and weight indices. Type 1 is the group with long and undersized upper body and straight body type since the face of the upper body is long relative to height and width, girth and depth are the smallest relative to weight, the breasts are somewhat fat, with a small extent of drooping and a straight back. Type 2 is the group that is considered fat relative to the body, has broad shoulders, drooping breasts with a wide space between them, and a back-bent upper body. Type 3 is the group that has a bent shape, the shortest upper body relative to height, and showing average obesity factors. 2. Indices of height and weight were used for factor analysis, cluster analysis, and discriminant analysis in order to classify upper body somatotype according to shape while excluding size factors of elderly women＇s upper body somatotype. The same method was used to compare and verify the result according to the absolute measurement and height index. Classification based on height and weight indices demonstrate that such somatotype classification minimizes the personal equation of body shape and it induces better classification based on shape as the results showed the highest cumulative sum of square(CUSUM) at 78.38% while six factors showed the smallest result and the hit rate for the classified three groups showed the highest result at 95.30%.