Towards Predicting Player Experience as Discrete Emotion Intensity Using Gameplay and Visual Data in a Multiplayer Game

Facial expression analysis has been shown to be an unintrusive and cheap method of player experience modelling. Output from such systems is then often closely tied to measures of basic discrete emotions. Despite this, little games user research attempts to predict the intensity of players' emotions collected in response to validated measures of discrete emotion, that can be more widely interpreted than those used in game specific settings. Prediction of experience as a function of discrete emotions using player facial expressions therefore presents opportunities for both the furthering of intelligent game adaptation, and the fields of serious games and emotional regulation. This study tests multiple approaches for the use, and selection, of features describing players' gameplay and facial activity during an online multiplayer game. Over 18 hours of visual and behavioural data collected from 37 participants is used to build predictors of the intensity for discrete emotions of relaxation, happiness, and desire as measured using the Discrete Emotions Questionnaire, reaching a model accuracy of 55%, 57% and 63% respectively. Models using facial expression features collected over the course of entire game levels show significantly better accuracy than models using gameplay or facial reaction features.