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Prediction of Body Mass Index from Facial Features of Females and Males
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- 발행기관
- 보안공학연구지원센터(IJBSBT) 바로가기
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- 간행물
- International Journal of Bio-Science and Bio-Technology SCOPUS 바로가기
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- 통권
- Vol.4 No.3 (2012.09)바로가기
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- 페이지
- pp.45-62
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A207081
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Human obesity has become a global epidemic. Body mass index (BMI) is clinically useful data for the diagnosis of overall adiposity. The purpose of this study was to identify normal and overweight patients based on facial characteristics extracted from subject image data, irrespective of the measurement of weight and height. In this paper, we propose a prediction method for normal and overweight from morphological facial characteristics that are associated with overweight and normal BMI statuses. A total of 1244 subjects participated in this study. The subjects were divided into 6 groups based on age- and gender-specific differences. The area under the receiver operating characteristics curve (AUC) and kappa of the prediction model ranged from 0.760 to 0.931, and from 0.401 to 0.586, respectively, for all groups, except for the group comprising females aged ≥61 years. Statistical analysis revealed many features that were significantly different between overweight and normal in the 6 groups. Furthermore, compact and useful feature sets were identified for BMI prediction using facial features in gender- and age-specific groups. We identified a relationship between facial morphology and BMI status, and the possibility of predicting the BMI status of individuals. Our results will facilitate the development of improved applications for age- and gender-specific groups in the fields of adiposity, facial recognition, and medicine.
목차
Abstract
1. Introduction
2. Methods
2.1. Subjects and Data Acquisition
2.2. Experimental Design
2.3. Area Under the Receiver Operating Characteristics Curve (AUC) and Kappa
3. Results and Discussion
3.1. Classification Results
3.2. Statistical Analysis of BMI and Facial Characteristics
3.3. Limitations
4. Summary
Acknowledgements
References
키워드
Classification Body mass index (BMI) Machine learning Relationship Facial morphology저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJBSBT) [Science & Engineering Research Support Center, Republic of Korea(IJBSBT)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Bio-Science and Bio-Technology
- 간기
- 격월간
- pISSN
- 2233-7849
- 수록기간
- 2009~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Bio-Science and Bio-Technology Vol.4 No.3
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