University of Montreal
Game Scenes Evaluation and Player’s Dominant Emotion Prediction
Abstract: In this paper, we present a solution for computer assisted emotional analysis of game session. The proposed approach combines eye movements and facial expressions to annotate the perceived game objects with the expressed dominate emotions. Moreover, our system EMOGRAPH (Emotional Graph) gives easy access to information about user experience and predicts player’s emotions. The prediction mainly uses both subjective measures through questionnaire and objective measures through brain wave activity (electroencephalography – EEG) combined with eye tracking data. EMOGRAPH’s method was experimented on 21 participants playing horror game “Outlast”. Our results show the effectiveness of our method in the identification of the emotions and their triggers. We also present our emotion prediction approach using game scene’s design goal (defined by OCC variables from the model of emotions’ cognitive evaluation of Ortony, Clore and Collins ) to annotate the player’s situation in a scene and machine learning algorithms. The prediction results are promising and would widen possibilities in game design.