Magister Scientiae - MSc (Computer Science) / A 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/6412 |
| Date | January 2018 |
| Creators | Deaney, Mogammat Waleed |
| Contributors | Venter, Isabella |
| Publisher | University of the Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Rights | University of the Western Cape |
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