# Game Difficulty Prediction Based on Facial Cues and Game Performance
## 5 Discussion  
Machine learning methods such as MLP, SVM, and DT have been applied in many fields, such as usage intentions for website prediction [40], stress recognition [41], user satisfaction prediction [42], etc. This paper uses machine learning classifiers to predict user-perceived game difficulty by learning representations from different user facial cues and game performance. The experimental results show that MLP acquired better performance than other classifiers on both two-class and three-class classification tasks. Unlike other traditional classifiers, MLP contains multiple hidden layers and nonlinear activation functions, which enables it to simulate complicated nonlinear interactions between inputs and outputs and handle more complex tasks. Therefore, it could achieve superior performance in our study, especially on the 3-class classification task with an imbalanced dataset. Additionally, classifiers such as SVM and DT, although they have also achieved promising results in previous studies, did not perform well on the three-class classification task. These results indicate that various methods are suitable for different classification tasks. [PERSON] et al. [19] also used a camera-based approach to capture user facial expressions and predict the game difficulty perceived by the user. However, using only facial expressions may not be sufficient, as we found that some participants' facial expression changes were not visible during the experiment, which may be because the game is relatively simple and does not evoke sufficient emotions, or they may deliberately control their expressions. Multimodal fusion can gather information from multiple dimensions and thus complement unimodal data. The classification performance of MLP improved by combining multimodal data, which is consistent with studies [21; 25; 27; 28]. Unlike these studies, which required the use of different hardware devices to collect user data (e.g., physiological signals, eye movements, etc.) in a restricted laboratory setting, our method could acquire multiple modalities of data from facial images recorded by a single camera, allowing us to collect data in any place without affecting the user's gaming experience. Furthermore, although the physiological signal method is also objective, the data acquisition and synchronization processes are more complicated than our method. Our study is based on a customized single-player parkour game. It is worth pointing out that the proposed framework could also apply to other game types, such as puzzle or  
Figure 9: Comparison of MLP with different input data modalities. Here, FE represents facial expression, GD is gaze direction, HP indicates head pose, and GP is game performance. Bold represents the best. action. It can be easy to capture facial cues when users play games, since most users' computers are equipped with front-facing cameras. However, when predicting the perceived difficulty, it would be inappropriate and inaccurate to directly employ the model trained in this study for another game unless the two games are similar in operation, mechanics, game settings, etc. User experience in different games should be different, and the user could be easily influenced by various aspects, including game content, graphics, other players, etc. Therefore, their facial cues and subjective feelings will be discrepant with our study. We should recollect the dataset and train various machine learning models for other games as we did in this study. In the future, we will consider using multiple games to collect and create a broad dataset to improve the generalization ability of the prediction model. Additionally, fusing the strengths of various models to develop new machine-learning models is also one of the directions that can be focused on in the future. Several limitations of this paper should be acknowledged. First, the participants in this study were graduate students and game enthusiasts from our university, which may introduce potential bias and limit the validity of the results.