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基于多尺度3D残余卷积神经网络的微表情识别.

Hongmei Jin1, Ning He1, Zhanli Li1

  • 1College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Mathematical biosciences and engineering : MBE
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多尺度3D残余卷积神经网络,用于准确的微表达式识别. 新型号显著改善了微妙的面部特征的检测,提高了在关键应用中的识别准确性.

关键词:
3D残余卷积神经网络 3D残余卷积神经网络注意力机制注意力机制歧视性的网络网络.识别微表情的功能多尺度特征提取多尺度特征提取

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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:

  • 准确的微表情识别对于心理治疗和刑事审讯等应用至关重要.
  • 挑战包括捕捉微妙,短暂的面部特征和提高识别性能.
  • 现有的方法难以有效地提取微表达的微妙特征.

研究的目的:

  • 提出一种用于增强微表达式识别的新型架构.
  • 为了应对捕捉微弱和短暂的面部特征的挑战.
  • 为了提高高精度微表情识别的准确性.

主要方法:

  • 开发了一个多尺度的3D残余卷积神经网络 (3D-ResNet50).
  • 使用微表达式的光流特征地图作为输入.
  • 集成的多尺度卷积模块和特征融合的注意力机制.
  • 使用具有多个输出通道的歧视性网络结构.

主要成果:

  • 实现了74.6% (SMIC),84.77% (SAMM) 和91.35% (CASME II) 的识别准确率.
  • 与现有的主流方法相比,证明了显著的改进.
  • 有效地提高了微表情识别性能和准确性.

结论:

  • 拟议的多尺度3D残余卷积神经网络提供了卓越的微表达式识别.
  • 这种新的架构有效地整合了空间和时间特征,以提高上下文意识.
  • 这项工作为高精度微表情识别研究提供了宝贵的参考.