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.

使用卷积神经网络在遮住眼睛和眉毛的全脸图像中准确识别七种不同的情绪，通过对虚拟现实游戏中玩家情感数据的收集与分析，提高游戏玩法分析，帮助游戏开发者创建更具吸引力和沉浸感的游戏体验。