Emotion detection is a crucial component of Games User Research (GUR), as it allows game developers to gain insights into players' emotional experiences and tailor their games accordingly. However, detecting emotions in Virtual Reality (VR) games is challenging due to the Head-Mounted Display (HMD) that covers the top part of the player's face, namely, their eyes and eyebrows, which provide crucial information for recognizing the impression. To tackle this we used a Convolutional Neural Network (CNN) to train a model to predict emotions in full-face images where the eyes and eyebrows are covered. We used the FER2013 dataset, which we modified to cover eyes and eyebrows in images. The model in these images can accurately recognize seven different emotions which are anger, happiness, disgust, fear, impartiality, sadness and surprise. We assessed the model's performance by testing it on two VR games and using it to detect players' emotions. We collected self-reported emotion data from the players after the gameplay sessions. We analyzed the data collected from our experiment to understand which emotions players experience during the gameplay. We found that our approach has the potential to enhance gameplay analysis by enabling the detection of players' emotions in VR games, which can help game developers create more engaging and immersive game experiences.

翻译：情感检测是游戏用户研究（GUR）的关键组成部分，它使游戏开发者能够洞察玩家的情感体验并据此调整游戏设计。然而，由于头戴式显示器（HMD）遮挡了玩家面部的上半部分（即提供表情识别关键信息的眼睛和眉毛区域），在虚拟现实（VR）游戏中检测情感颇具挑战性。为解决这一问题，我们采用卷积神经网络（CNN）训练模型，在覆盖眼睛和眉毛的全脸图像中预测情感。我们使用经修改的FER2013数据集（人为覆盖图像中的眼睛和眉毛区域）。该模型能准确识别愤怒、快乐、厌恶、恐惧、平静、悲伤和惊讶这七种情感。我们通过在两款VR游戏中进行测试来评估模型性能，并利用该模型检测玩家的情感。在游戏环节结束后，我们收集玩家自报的情感数据。通过分析实验数据，我们揭示了玩家在游戏过程中经历的情感类型。研究表明，该方法通过实现VR游戏中玩家情感的检测，具有增强游戏分析的潜力，可帮助游戏开发者创造更具沉浸感和吸引力的游戏体验。