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。

虚拟现实游戏中的面部情感识别

Facial Emotion Recognition in VR Games

This paper proposes a novel deep reinforcement learning algorithm to perform
automatic analysis and detection of gameplay issues in complex 3D navigation
environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method
combines curiosity and imitation learning to train agents to methodically
explore in the proximity of known trajectories derived from expert
demonstrations. We show how CCPT can explore complex environments, discover
gameplay issues and design oversights in the process, and recognize and
highlight them directly to game designers. We further demonstrate the
effectiveness of the algorithm in a novel 3D navigation environment which
reflects the complexity of modern AAA video games. Our results show a higher
level of coverage and bug discovery than baselines methods, and it hence can
provide a valuable tool for game designers to identify issues in game design
automatically.

本文提出了一种新型的深度强化学习算法，通过结合好奇心和仿真学习以训练智能代理，从而在复杂的三维导航环境中自动分析和检测游戏问题，并直接向游戏设计师总结其在游戏设计中的问题和设计疏忽。通过在全新的反映现代 AAA 视频游戏复杂性的三维导航环境中进行实验，证明了 CCPT 比基线方法具有更高的覆盖率和 Bug 发现率，从而为游戏设计师提供了一种有价值的工具，可以自动识别游戏设计中的问题。