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dc.contributor.authorZhang, Licheng 16:57:19 (GMT) 16:57:19 (GMT)
dc.description.abstractVideo games can generate different emotional states and affective reactions, but it can sometimes be difficult for a game’s visual designer to predict the emotional response a player might experience when designing a game or game scene. In this thesis, I conducted a study to collect emotional responses to video game images. I then used that data to both confirm past research that suggests images can be used to predict affect and to build a model for predicting emotion that is specific to games. I built both a linear regression model and three neural network models to predict affective response and found that the neural net that leveraged ResNet-50 was most effective. I then incorporated that model into a Unity plug-in so that designers can use it to predict affect of players in real time.en
dc.publisherUniversity of Waterlooen
dc.subjectaffective computingen
dc.subjectvideo gamesen
dc.subjectmachine learningen
dc.titleDesigning a Unity Plugin to Predict Expected Affect in Games Using Biophiliaen
dc.typeMaster Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorHancock, Mark
uws.contributor.advisorVogel, Daniel
uws.contributor.affiliation1Faculty of Mathematicsen

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