Rupenga, Moses2018-02-192018-02-192017Rupenga, Moses (2017) A study of the temporal relationship between eye actions and facial expressions, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/24021>https://hdl.handle.net/10539/24021A 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, 2017Facial 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.Online resource (xiv, 80 leaves)enFacial expressionEye--MovementsMachine learningSupport vector machinesThesis