  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.

Human-Computer Interaction;Computer Vision and Pattern Recognition;Machine Learning