Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier

Bob Zhang, B. V.K. Vijaya Kumar, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

62 Citations (Scopus)


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%.
Original languageEnglish
Article number6675828
Pages (from-to)1027-1033
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Issue number4
Publication statusPublished - 1 Jan 2014


  • Color feature
  • diabetes mellitus (DM)
  • facial block
  • facial color gamut
  • sparse representation classifier (SRC)

ASJC Scopus subject areas

  • Biomedical Engineering


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