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通过光场摄像头增强面部表情识别
Sabrine Djedjiga Oucherif1, Mohamad Motasem Nawaf2, Jean-Marc Boï2
1Institut de Mathématiques de Marseille (IMM), CNRS, Aix-Marseille University, 13009 Marseille, France.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究使用来自光场摄像机的多式数据增强了面部表情识别 (FER). 结合子光圈,深度和全焦图像,比单模方法取得了更高的精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 面部表情识别 (FER) 对人机交互至关重要.
- 现有的FER系统通常依赖于单一的模式,限制了全面的分析.
- 光场摄像机提供丰富的数据,包括深度和亚光圈信息,以改进FER.
研究的目的:
- 通过研究多式联战略,开发一个更有效和更全面的FER系统.
- 在决策和特征层面评估不同聚变技术的性能.
- 为了利用从亚光圈 (SA),全焦 (AiF) 和深度图像中获得的补充信息.
主要方法:
- 使用EfficientNetV2-S,在AffectNet上进行预训练,作为脊柱卷积神经网络.
- 采用双向门式反复单元 (BiGRU) 来处理SA图像.
- 研究了各种决策层面和特征层面的融合策略,用于多式联运数据集成.
主要成果:
- 使用SA图像的单模模型实现了最先进的性能 (88.13%的特定对象,91.88%的独立对象准确度).
- 与单模方法相比,多模融合显著提高了FER准确性.
- 与平均权重的决策级融合产生了最高的准确性 (90.13%的主题特定,93.33%的主题独立).
结论:
- SA,AiF和深度图像的多式融合提高了FER系统的准确性和稳定性.
- 拟议的方法优于现有的FER方法.
- 决策层面的融合是一种高度有效的战略,用于整合补充的面部表情信息.
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