对于半监督的面部表情识别来说,增强了适应性信心率.
IEEE transactions on pattern analysis and machine intelligence
|September 22, 2025
概括
本研究介绍了面部表情识别 (FER) 中的半监督学习 (SSL) 的增强适应性信心率 (EACM). EACM 改进了未标记面部数据的使用方式,优于传统方法.
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:
- 面部表情识别 (FER) 通常需要大量的标记数据,而这些数据的获取成本很高.
- 半监督学习 (SSL) 通过利用丰富的未标记数据提供了一个解决方案.
- 现有的FER的SSL方法在处理不同类别的信心和高效的未标记数据利用方面存在局限性.
研究的目的:
- 为提高FER中的SSL提出一个增强的自适应性保证金 (EACM).
- 为了解决FER中固定的值SSL方法的局限性.
- 为了提高未标记的面部表情样本的有效利用.
主要方法:
- 开发了EACM,具有针对不同的面部表情类别量身定制的动态值.
- 将未标记的数据分成基于差别处理的置信率的子集.
- 实施了高可信度样本的伪标签,以及低可信度样本的特征级别对比目标.
主要成果:
- 在基于图像和基于视频的FER数据集上,EACM表现出卓越的性能.
- 拟议的方法在半监督的环境中明显超过了完全监督的基线.
- EACM显示了利用跨数据集的未标记样本来提高性能的潜力.
结论:
- 在SSL中,EACM有效地解决了对特定类别的信心和高效的非标记数据使用的挑战.
- 该方法提供了一种实用和有效的方法,以有限的标记数据来增强FER系统.
- 通过跨数据集学习,EACM为改善FER模型概括和性能提供了一个有希望的方向.
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