NDLI: Facial color analysis of Overweight-Obesity and its relationship to Healthy and Diabetes Mellitus using statistical pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Ting Shu Zhang, B.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Univ. of Macau, Macau, China (Ting Shu; Zhang, B.)
Abstract	It is well known in the medical community that being overweight/obese is damaging to one's health, and increases the risk of diabetes. However, this is currently investigated via physiological/biochemical experiments considered to be invasive, or through the body mass index of a population. In this paper we propose a non-invasive method with statistical pattern recognition to analyze the relationship between three classes: Overweight/Obesity (140 samples), Healthy (125 samples), and Diabetes Mellitus (284 samples), using facial block color features. Facial images labeled by medical doctors practicing western medicine were captured by a specifically designed non-invasive device adopting color correction. After image capture, color features were extracted from the facial blocks. When comparing the three classes in groups of two by calculating their mean Euclidean distance, the results indicate Overweight/Obesity and Diabetes Mellitus are closer than the other combinations. This supports the claim that being overweight/obese is highly correlated with the presence of diabetes.
Starting Page	1
Ending Page	4
File Size	1343206
Page Count	4
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7372724
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Feature extraction Diabetes Euclidean distance Obesity Color Pattern recognition statistical pattern recognition overweight/obese Diabetes Mellitus non-invasive
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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