  This paper compares and explores the performance of both mobile device camera
and laptop camera as convenient tool for capturing images for non-invasive
detection of Diabetes Mellitus (DM) using facial block texture features.
Participants within age bracket 20 to 79 years old were chosen for the dataset.
12mp and 7mp mobile cameras, and a laptop camera were used to take the photo
under normal lighting condition. Extracted facial blocks were classified using
k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). 100 images were
captured, preprocessed, filtered using Gabor, and iterated. Performance of the
system was measured in terms of accuracy, specificity, and sensitivity. Best
performance of 96.7% accuracy, 100% sensitivity, and 93% specificity were
achieved from 12mp back camera using SVM with 100 images.
