# Game Difficulty Prediction Based on Facial Cues and Game Performance
## 1 Introduction  
Games, as the most common entertainment form, have a large audience. Difficulty is a fundamental concept in games [1, 2]. Excessive difficulty may cause users anxiety, while insufficient difficulty may lead to boredom [3]. To attract players of different levels, game developers usually provide various difficulty settings for players to choose from or design algorithms to automatically adjust the game difficulty to match the player's level. All of these require assessing the user's perceived game difficulty in advance [4]. Furthermore, game difficulty could impact player motivation [5, 6], self-confidence [7], performance [8], and engagement [9]. Therefore, it is necessary and meaningful to predict game difficulty accurately. Most current studies utilize success rates, life points, and completion times to assess game difficulty [10, 11, 12]. Although they can predict game difficulty to some extent, they hardly take user data into account. Some methods collect physiological signals when users play the game and employ machine learning methods to classify the difficulty [13, 14]. Although physiological signals are more objective, their acquisition process often requires some sensor devices connected to users, which may be invasive and affect the game experience. Additionally, it is laborious to process physiological data. These can lead to a lower accuracy of prediction. This paper presents a method for predicting game difficulty based on multiple facial cues and game performance. Unlike those cumbersome physiological signal-based methods, our method is non-invasive and requires only one camera to record user data. Specifically, we use various computer vision methods to obtain multimodal facial features during gameplay, including facial expressions, gaze directions, and head poses. Multimodal features are collected because they can compensate for the shortcomings of a single modality. Then, we build a game difficulty dataset by combining these facial cues with the user's game performance as input and labeling it with the user's subjective difficultyscores. Subsequently, we train several machine learning classifiers and compare them on two tasks: two-class classification and three-class classification. The experimental results show that MLP, a forward-structured neural network that contains several fully connected layers, outperforms other machine learning classifiers on these two tasks. In addition, we compare the effects of different inputs on prediction performance, and the results further demonstrate the effectiveness of combining multimodal features. The main objective of this paper is to provide a method for automatically recognizing user-perceived game difficulty, which can help designers modify games based on the difference between predicted difficulty and designed difficulty. In addition, the game level can be dynamically adjusted according to the estimated difficulty. For example, when users feel that the game is difficult or are challenged when playing, the system could reduce the game difficulty accordingly. Additionally, our method can be introduced not only to game difficulty prediction but also to any research related to user perception. In summary, the main contributions of this paper are as follows:  
1. We created a game difficulty dataset that uses facial expressions, gaze directions, head poses, the number of failures, and game scores as inputs, which subjective game difficulty ratings as labels. 2. We compared the performance of different machine learning algorithms on the collected dataset and performed ablation experiments to compare the effects of various inputs on prediction accuracy. The results showed that MLP achieved the best results on all evaluation metrics, and combining multiple features yielded the highest performance. The remainder of the paper is organized as follows: Section 2 reviews the studies concerning difficulty prediction and multimodal fusion. Section 3 introduces the procedures of the experiment and dataset collection.