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.

在本文中，我们介绍了面部表情分析（FEA）作为预测游戏难度的一种方法以及一种建模机制，在此基础上，我们开发了针对单人街机游戏的游戏难度调整算法。我们的主要贡献是实施在线且个性化的游戏个性化系统。在有限元分析的基础上，我们的系统能够在实际游戏过程中不间断地使游戏的难度级别适应各个玩家。

具体来说，我们研究（a）如何通过面部表情分析来衡量感知到的游戏难度，以及（b）面部表情数据如何为玩家的行为建模并预测其情感状态。

实验结果表明，不同的游戏难度设置可以与不同的玩家情绪相关（显示在面部表情中）。此外，成功地应用了基于面部表情分析的模型来计算适合个人玩家的适当难度设置。从这些结果，我们可以得出结论，通过FEA可以实现有效的游戏个性化。