Modeling and adjusting in-game difficulty based on facial expression analysis

Abstract

In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.