Yanushkevich, Svetlana N.Ejindu, Oluchukwu Roseline2019-05-142019-05-142019-05-14Ejindu, O. R. (2019). Detecting Abnormalities in Thermal Pattern of Faces for Healthcare Applications (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110344In this work, we propose a novel method of applying deep learning technique in thermal image processing and analysis for healthcare application. It addresses detection of abnormal thermal patterns, thus identifying, in particular, patterns of elevated temperature that indicate fever, hypothermia and related abnormalities. Temperature estimation is performed based on the analysis of regions-of-interest from the thermal images of human faces. Another focus of this work is to investigate thermal effects of alcohol intoxication. We applied the deep learning approach on 16,000 usable images of 40 subjects from a publicly-available Drunk-Sober database. Two Convolutional Neural Network architectures were investigated for the task of processing of two regions of interest - the forehead and the eyes. The accuracy of the neural network classifiers to predict subject’s insobriety using the forehead and eye regions-of-interest reached 95.5% and 96.67%, respectively, compared to the best-known results on the same data using a non-deep neural networks. To boost the accuracy of classification, both the feature-level and the score-level fusion were applied as well, thus improving the accuracy to 96.92%.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Engineering--Electronics and Electricalmaster thesis10.11575/PRISM/36516