Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12554
Title: Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier
Authors: Zhang, B
Vijaya Kumar, BVK
Zhang, D 
Keywords: Color feature
Diabetes mellitus (DM)
Facial block
Facial color gamut
Sparse representation classifier (SRC)
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on biomedical engineering, 2014, v. 61, no. 4, 6675828, p. 1027-1033 How to cite?
Journal: IEEE transactions on biomedical engineering 
Abstract: Diabetes mellitus (DM) is gradually becoming an epidemic, affecting almost every single country. This has placed a tremendous amount of burden on governments and healthcare officials. In this paper, we propose a new noninvasive method to detect DM based on facial block color features with a sparse representation classifier (SRC). A noninvasive capture device with image correction is initially used to capture a facial image consisting of four facial blocks strategically placed around the face. Six centroids from a facial color gamut are applied to calculate the facial color features of each block. This means that a given facial block can be represented by its facial color features. For SRC, two subdictionaries, a Healthy facial color features subdictionary and DM facial color features subdictionary, are employed in the SRC process. Experimental results are shown for a dataset consisting of 142 Healthy and 284 DM samples. Using a combination of the facial blocks, the SRC can distinguish Healthy and DM classes with an average accuracy of 97.54%.
URI: http://hdl.handle.net/10397/12554
ISSN: 0018-9294
EISSN: 1558-2531
DOI: 10.1109/TBME.2013.2292936
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