Pleasure-arousal-outlier model for quantitative evaluation of game experiences

Abstract

This study proposes a pleasure-arousal-outlier (PAO) model to quantify the experiences derived from games. The proposed technique identifies pleasure, arousal, and outlier levels based on the facial expression of a user, keyboard input information, and mouse movement information received from a multimodal interface and then projects the received information in three-dimensional space to quantify the game experience state of the user. Facial expression recognition and distribution, eye blink, and eye glance concentration graphs were introduced to determine the immersion levels of games. A convolutional neural network-based facial expression recognition algorithm and dynamic time warp-based outlier behavior detection algorithm were adopted to obtain numerical values required for the PAO model evaluation. We applied the proposed PAO model for first-person shooter games and consequently acquired evaluation result values that were clearly distinguishable for different players. Such information allows player experiences to be quantitatively evaluated when designing game levels.