多模式注意力动态融合网络用于面部微表情识别
Hongling Yang1, Lun Xie2, Hang Pan1
1Department of Computer Science, Changzhi University, Changzhi 046011, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
面部微表情识别通过一种新的模型得到了改进,该模型增强了局部细节,并将面部图像特征与动作单元数据融合在一起. 这种多式模式的动态注意力融合网络 (MADFN) 增强了情感特征的歧视.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 面部微表情通过行动单位的组合传达情绪变化.
- 行动单元提供辅助数据来增强面部微表情识别.
- 现有的方法往往忽视了动作单元对面部图像特征提取的影响.
研究的目的:
- 为微表情识别提出一种新的局部细节特征增强模型.
- 通过考虑行动单元对特征提取的影响来解决当前融合技术的局限性.
- 引入多式联运动态注意力融合网络 (MADFN).
主要方法:
- 使用带有可学习类代码的蒙面自动编码器,通过删除低表达性区域来完善微表达图像.
- 采用动作单元动态融合模块,将动作单元表示与图像特征集成.
- 开发了多式模式动态注意力融合网络 (MADFN) 以提高特征表示.
主要成果:
- 在基准数据集上,MADFN模型实现了最先进的性能:SMIC (81.71%),CASME II (82.11%) 和SAMM (77.21%).
- 通过图像和行动单元数据的有效融合,证明了面部图像情感特征的改进歧视.
- 在SMIC,CASME II,SAMM和3DB-Combined数据集中验证了模型的有效性.
结论:
- 拟议的MADFN模型通过改善情绪特征的歧视,显著提高了面部微表情识别.
- 动态地与图像特征集成行动单元信息对于强大的微表达式分析至关重要.
- 这些发现凸显了MADFN在微表情识别领域的发展潜力.
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