Cognitive reappraisal of emotion quantification techniques

Abstract

Emotion is an extremely important factor in the manner in which people act, interact newlinewith each other, and make choices. As human-computer interaction (HCI) and newlineaffective computing have grown, emotion recognition systems have been highly newlinesought after. Yet, the majority of these systems are founded upon classifying models newlineof emotion, making them unable to capture the richness and dynamics of emotions. newlineEmotions are not discrete and static but they are of varying intensities and newlineexpressions depending on the person. This thesis resolves these problems by newlineintroducing a new approach to emotion modelling by quantification of emotions, newlineturning emotion recognition from simple class labelling into a process that measures newlineemotions more finely grained. newlineThis study draws concepts from various emotion models to demonstrate the need newlineto quantify emotions that not only indicate type but also intensity. The research newlinebuilds an emotion measurement library that primarily uses facial expressions since newlinethey are simple to record and do not invade privacy. The approach employs facial newlinelandmark detection, polygonal facial segmentation, deep learning feature newlineextraction, and intensity scoring to quantify emotions on a scale. Unlike normal newlineclassifiers, this system can distinguish various intensities of the same emotion (e.g., newlinelow, medium, or extreme anger), providing a more precise and context-specific newlinemeans of expressing emotions. newline