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      사상체질 판별을 위한 측면 얼굴 이미지에서의 특징 검출 = Side Face Features` Biometrics for Sasang Constitution

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      https://www.riss.kr/link?id=A103035314

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      다국어 초록 (Multilingual Abstract)

      There are four types of human beings according to the Sasang Typology. Oriental medical doctors frequently prescribe healthcare information and treatment depending on one`s type. The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people`s side face. There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose. To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean`s left side facial photographs. Experiment results shows that the accuracy of our system is increased 17.99% after we combine the front face features with the side face features, instead of using the front face features only.
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      There are four types of human beings according to the Sasang Typology. Oriental medical doctors frequently prescribe healthcare information and treatment depending on one`s type. The feature ratios (Table 1) on the human face are the most important cr...

      There are four types of human beings according to the Sasang Typology. Oriental medical doctors frequently prescribe healthcare information and treatment depending on one`s type. The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people`s side face. There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose. To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean`s left side facial photographs. Experiment results shows that the accuracy of our system is increased 17.99% after we combine the front face features with the side face features, instead of using the front face features only.

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      참고문헌 (Reference)

      1 "Toward Automation of Learning the State Self-organization Problem for A Face Recognizer" 384-389, 1998.

      2 "Sasang Constitution Classification Using Shape Analysis of Face" 13 (13): 423-426, 2006.

      3 "Rule-based Face Detection in Frontal Views" 2537-2540, 1997.

      4 "Integration of Eigentemplate and Structure Matching for Automatic Facial Feature Detection" 94-99, 1998.

      5 "Human Facial Feature Extraction for Face Interpretation and Recognition" 25 (25): 1435-1444, 1992.

      6 "Feature Recognition: Features Versus Templates" 15 (15): 1042-1052, 2002.

      7 "Face Recognition Under Varying Pose" 1461-1472, 1993.

      8 "Ear Biometrics Based on Geometrical Feature Extraction" 5 (5): 84-95, 2005.

      9 "Design on Sasang Constitution Classification System Using Face Morphologic Analysis" 13 (13): 97-100, 2006.

      10 "Anthropometric Standardization Reference Manual" Human Kinetics Books 1998.

      1 "Toward Automation of Learning the State Self-organization Problem for A Face Recognizer" 384-389, 1998.

      2 "Sasang Constitution Classification Using Shape Analysis of Face" 13 (13): 423-426, 2006.

      3 "Rule-based Face Detection in Frontal Views" 2537-2540, 1997.

      4 "Integration of Eigentemplate and Structure Matching for Automatic Facial Feature Detection" 94-99, 1998.

      5 "Human Facial Feature Extraction for Face Interpretation and Recognition" 25 (25): 1435-1444, 1992.

      6 "Feature Recognition: Features Versus Templates" 15 (15): 1042-1052, 2002.

      7 "Face Recognition Under Varying Pose" 1461-1472, 1993.

      8 "Ear Biometrics Based on Geometrical Feature Extraction" 5 (5): 84-95, 2005.

      9 "Design on Sasang Constitution Classification System Using Face Morphologic Analysis" 13 (13): 97-100, 2006.

      10 "Anthropometric Standardization Reference Manual" Human Kinetics Books 1998.

      11 "An Alternative Way to Individualized Medicine: Psychological and Physical Traits of Sasang Typology" 9 (9): 519-528, 2003.

      12 "A Thresholding Selection Method from Gray-scale Histogram" 9 (9): 62-66, 1979.

      13 "A Novel Approach to Detect and Correct Highlighted Face Region in Color Image" 7-12, 2003.

      14 "A Morphorlogical Study of Ear, Eye, Nose and Month according to the Sasang Constitution" 1998.

      15 "A Morphologic Study of Sasang Constitution" 1998.

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2013-11-05 학술지명변경 외국어명 : Journal of Korean Society for Internet Information -> Journal of Internet Computing and Services KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2005-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.55 0.55 0.63
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.64 0.6 0.85 0.03
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