VR游戏中的面部情绪识别
Facial Emotion Recognition in VR Games
AI总结：
针对VR游戏中HMD遮挡眉眼导致面部情绪识别困难的问题，本研究用修改后的FER2013数据集训练CNN模型识别七种情绪，经两款VR游戏测试验证，该方法可助力开发者打造更具沉浸感的游戏体验。
摘要：
情绪检测是游戏用户研究（Games User Research, GUR）的关键组成部分，它能让游戏开发者深入了解玩家的情绪体验，并据此调整游戏设计。然而，在虚拟现实（Virtual Reality, VR）游戏中检测情绪颇具挑战性，因为头戴式显示设备（Head-Mounted Display, HMD）会遮挡玩家面部的上半部分，即眼睛和眉毛，而这两个部位为识别情绪印象提供了关键信息。为解决这一问题，我们使用卷积神经网络（Convolutional Neural Network, CNN）训练了一个模型，用于预测眼睛和眉毛被遮挡的全脸图像中的情绪。我们使用了FER2013数据集，并对其进行修改以遮挡图像中的眼睛和眉毛。该模型在这些图像上能够准确识别七种不同的情绪，分别是愤怒、快乐、厌恶、恐惧、中立、悲伤和惊讶。\n我们通过在两款VR游戏中测试该模型并利用其检测玩家情绪，评估了模型的性能。我们在游戏环节结束后收集了玩家自我报告的情绪数据。我们对实验收集的数据进行了分析，以了解玩家在游戏过程中会体验到哪些情绪。研究发现，我们的方法能够实现VR游戏中的玩家情绪检测，有望增强游戏分析能力，帮助游戏开发者打造更具吸引力和沉浸感的游戏体验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
