具有渐进特征融合的层次关注网络,用于面部表情识别和面部表情识别
1School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, PR China; Engineering Research Center of Embedded System Integration, Ministry of Education. Xi'an 710129, PR China; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi'an 710129, PR China.
概括
这项研究引入了一种新的层次关注网络,用于野外的面部表情识别 (FER). 该模型有效地处理像姿势和遮这样的变化,在识别图像中的情绪方面实现了卓越的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在不受控制的环境中,面部表情识别 (FER) 很困难,原因是姿势变化,遮蔽和照明变化.
- 注意力机制通过专注于相关信息来改善FER,但现有的方法忽略了跨网络层的张量大小变化.
研究的目的:
- 为强大的FER提出一个层次化的注意力网络,具有渐进的特征融合.
- 解决固定注意力机制在处理动态变化的特征张量尺寸方面的局限性.
主要方法:
- 一个多样化的特征提取模块汇集了本地,全球,低级,高级和梯度特征.
- 一个层次关注模块 (HAM) 逐渐融合特征,增强歧视性面部区域并抑制不相关区域.
- 该模型使用特征聚合块来利用补充信息.
主要成果:
- 拟议的层次关注网络显著提高了FER的性能.
- 该模型与现有的FER方法相比,显示出更高的准确性.
- 实验结果验证了多种特征提取和HAM的有效性.
结论:
- 具有渐进特征融合的层次关注网络为FER在自然环境中提供了强大的解决方案.
- 提出的方法有效地减轻了由姿势变化,遮蔽和照明所带来的挑战.
- 这种方法推进了自动面部表情识别的最新技术.
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