混合域一致性基于约束的深度神经网络用于面部表情识别
Xiaoliang Zhu1, Junyi Sun1, Gendong Liu1
1National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一个混合域一致性网络 (HDCNet),通过解决诸如阻塞和照明等挑战来改善面部表情识别 (FER). 新型网络提高了准确性,而不需要额外的标签.
科学领域:
- 计算机视觉 计算机视觉
- 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) 性能受到不均照明,遮蔽和注释主观性等因素的阻碍.
- 传统的FER方法与这些现实世界的复杂性作斗争,需要更强大的方法.
研究的目的:
- 提出一种新的混合域一致性网络 (HDCNet),以改进面部表情识别.
- 通过特征约束,通过整合空间和通道域一致性来提高FER准确性.
- 开发一种不需要额外标记数据的方法,以满足其注意力一致性约束.
主要方法:
- 在HDCNet使用的特征约束方法结合空间和道域一致性.
- 它通过比较原始和增强的面部表情图像来挖掘注意力一致性特征.
- 混合域的一致性损失函数约束特征表达并优化网络权重.
主要成果:
- 在RAF-DB和AffectNet数据集上的实验证明了更好的分类准确性.
- 拟议的HDCNet实现了比现有方法准确度的提高,精度从0.3%到3.84%.
- 注意一致性限制有效地运行,不需要额外的标签.
结论:
- 混合域一致性网络 (HDCNet) 在面部表情识别方面取得了重大进展.
- 拟议的方法有效地解决了FER数据集中常见的挑战,从而提高了准确性.
- HDCNet为面部表情识别任务提供了一个强大的,标签效率高的方法.
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