在虚拟现实中实现通用情绪识别,通过将自然表情与动作面部表情相结合
Rahat Rizvi Rahman1, Hee Yun Choi2, Joonghyo Lim2
1Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
开发精确的虚拟现实 (VR) 情感识别需要结合演戏和自然的面部表情. 联合训练的模型显示了适应性VR体验的改进概括.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 情感计算是一种情感计算.
背景情况:
- 在虚拟现实 (VR) 中面部情绪识别 (FER) 对于个性化体验至关重要.
- 现有的FER模型经常使用演技表达,这与VR中的自发情绪不同.
- 在沉浸式VR互动期间识别自然主义情绪存在差距.
研究的目的:
- 为VR开发和评估可通用的情绪识别模型.
- 通过演戏和自然的面部表情来研究联合学习.
- 为了提高VR情绪识别系统的准确性和稳定性.
主要方法:
- 整合了两个数据集:一个是自然的VR情绪,另一个是表演表情,使用Meta Quest Pro.
- 评估了各种模型架构:卷积网络,域对抗网络和专家混合模型.
- 分析了面部动作单位 (AU),以了解表情差异和共享的表现.
主要成果:
- 在综合操作和自然数据上训练的模型表现出优异的跨领域概括性.
- 领域对抗和专家混合模型在自然和混合情绪任务中取得了最高的准确性.
- 对于自然与行为情绪,观察到明显的面部动作单元 (AU) 模式,可概括的模型学习集成表示.
结论:
- 从演戏和自然的面部表情中共同学习显著提高了VR情感识别.
- 像域对抗和专家混合等先进的模型架构对于这个任务是有效的.
- 弥合演技和自然表达之间的差距是VR中强大而准确的情感计算的关键.
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