A Comparison of Machine Learning Techniques for Facial Expression Recognition

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dc.contributor.advisorVenter, Isabella
dc.contributor.authorDeaney, Mogammat Waleed
dc.date.accessioned2018-09-18T11:03:44Z
dc.date.accessioned2024-10-30T14:00:37Z
dc.date.available2018-12-31T22:10:06Z
dc.date.available2024-10-30T14:00:37Z
dc.date.issued2018
dc.descriptionMagister Scientiae - MSc (Computer Science)
dc.description.abstractA machine translation system that can convert South African Sign Language (SASL) video to audio or text and vice versa would be bene cial to people who use SASL to communicate. Five fundamental parameters are associated with sign language gestures, these are: hand location; hand orientation; hand shape; hand movement and facial expressions. The aim of this research is to recognise facial expressions and to compare both feature descriptors and machine learning techniques. This research used the Design Science Research (DSR) methodology. A DSR artefact was built which consisted of two phases. The rst phase compared local binary patterns (LBP), compound local binary patterns (CLBP) and histogram of oriented gradients (HOG) using support vector machines (SVM). The second phase compared the SVM to arti cial neural networks (ANN) and random forests (RF) using the most promising feature descriptor|HOG|from the rst phase. The performance was evaluated in terms of accuracy, robustness to classes, robustness to subjects and ability to generalise on both the Binghamton University 3D facial expression (BU-3DFE) and Cohn Kanade (CK) datasets. The evaluation rst phase showed HOG to be the best feature descriptor followed by CLBP and LBP. The second showed ANN to be the best choice of machine learning technique closely followed by the SVM and RF.
dc.identifier.urihttps://hdl.handle.net/10566/16912
dc.language.isoen
dc.publisherUniversity of the Western Cape
dc.rights.holderUniversity of the Western Cape
dc.subjectFacial expression recognition, Feature extraction, Machine learning, Support vector machine, Random forest, Arti cial neural network, Local binary patterns, Compound local binary patterns, Histogram of oriented gradients
dc.titleA Comparison of Machine Learning Techniques for Facial Expression Recognition

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