A dissertation submitted in ful llment of the requirements for the
degree of Master of Science
in the
School of Computer Science and Applied Mathematics
Faculty of Science
August 15, 2017 / Facial expression recognition is one of the most common means of communication used
for complementing spoken word. However, people have grown to master ways of ex-
hibiting deceptive expressions. Hence, it is imperative to understand di erences in
expressions mostly for security purposes among others. Traditional methods employ
machine learning techniques in di erentiating real and fake expressions. However, this
approach does not always work as human subjects can easily mimic real expressions with
a bit of practice. This study presents an approach that evaluates the time related dis-
tance that exists between eye actions and an exhibited expression. The approach gives
insights on some of the most fundamental characteristics of expressions. The study fo-
cuses on nding and understanding the temporal relationship that exists between eye
blinks and smiles. It further looks at the relationship that exits between eye closure and
pain expressions. The study incorporates active appearance models (AAM) for feature
extraction and support vector machines (SVM) for classi cation. It tests extreme learn-
ing machines (ELM) in both smile and pain studies, which in turn, attains excellent
results than predominant algorithms like the SVM. The study shows that eye blinks
are highly correlated with the beginning of a smile in posed smiles while eye blinks are
highly correlated with the end of a smile in spontaneous smiles. A high correlation is
observed between eye closure and pain in spontaneous pain expressions. Furthermore,
this study brings about ideas that lead to potential applications such as lie detection
systems, robust health care monitoring systems and enhanced animation design systems
among others. / MT 2018
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/24021 |
| Date | January 2017 |
| Creators | Rupenga, Moses |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (xiv, 80 leaves), application/pdf |
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