Emotion recognition promotes the evaluation and enhancement of Virtual Reality (VR) experiences by providing emotional feedback and enabling advanced personalization. However, facial expressions are rarely used to recognize users' emotions, as Head-Mounted Displays (HMDs) occlude the upper half of the face. To address this issue, we conducted a study with 37 participants who played our novel affective VR game EmojiHeroVR. The collected database, EmoHeVRDB (EmojiHeroVR Database), includes 3,556 labeled facial images of 1,778 reenacted emotions. For each labeled image, we also provide 29 additional frames recorded directly before and after the labeled image to facilitate dynamic Facial Expression Recognition (FER). Additionally, EmoHeVRDB includes data on the activations of 63 facial expressions captured via the Meta Quest Pro VR headset for each frame. Leveraging our database, we conducted a baseline evaluation on the static FER classification task with six basic emotions and neutral using the EfficientNet-B0 architecture. The best model achieved an accuracy of 69.84% on the test set, indicating that FER under HMD occlusion is feasible but significantly more challenging than conventional FER.

翻译：情感识别通过提供情感反馈和实现高级个性化，促进虚拟现实（VR）体验的评估与优化。然而，由于头戴式显示器（HMD）会遮挡面部上半部分，面部表情很少被用于识别用户情绪。为解决此问题，我们开展了一项包含37名参与者的研究，参与者体验了我们开发的新型情感化VR游戏EmojiHeroVR。所收集的数据库EmoHeVRDB（EmojiHeroVR Database）包含1,778种重演情感的3,556张标注面部图像。针对每张标注图像，我们还提供了标注时刻前后直接记录的29帧额外图像，以支持动态人脸表情识别（FER）。此外，EmoHeVRDB还包含每帧图像通过Meta Quest Pro VR头显捕捉的63种面部表情激活数据。基于本数据库，我们采用EfficientNet-B0架构对包含六种基本情绪及中性情绪的静态FER分类任务进行了基线评估。最佳模型在测试集上取得了69.84%的准确率，这表明HMD遮挡下的FER具有可行性，但相比传统FER面临显著更大的挑战。