Detecting Abnormalities in Thermal Pattern of Faces for Healthcare Applications

dc.contributor.advisorYanushkevich, Svetlana N.
dc.contributor.authorEjindu, Oluchukwu Roseline
dc.contributor.committeememberNowicki, Edwin Peter
dc.contributor.committeememberAlim, Usman Raza
dc.date2019-11
dc.date.accessioned2019-05-14T21:01:44Z
dc.date.available2019-05-14T21:01:44Z
dc.date.issued2019-05-14
dc.description.abstractIn 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%.en_US
dc.identifier.citationEjindu, 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.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36516
dc.identifier.urihttp://hdl.handle.net/1880/110344
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.en_US
dc.subject.classificationEngineering--Electronics and Electricalen_US
dc.titleDetecting Abnormalities in Thermal Pattern of Faces for Healthcare Applicationsen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrue
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