title: Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier.
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%.
authors.forename: Bob
authors.initials: B
authors.lastname: Zhang
authors.forename: B V K
authors.initials: BV
authors.lastname: Vijaya kumar
authors.forename: David
authors.initials: D
authors.lastname: Zhang
mesh_terms: D016022:Case-Control Studies; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000503:physiopathology*; D005145:Face / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012867:Skin / Q000473:pathology*