结合固定重量的arcFace损失和视觉变压器,用于面部表情识别
Yunhao Xu1, Xinran Duan1, Peihao Fan1
1College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究为视觉变压器 (ViT) 模型引入了重量受约束的ArcFace损失,通过稳定重量优化来提高面部表情识别精度和计算效率.
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) 受到类内变异的挑战.
- 像ArcFace损失这样的现有方法提高了可分离性,但没有限制类中心分布.
- 深度学习,特别是视觉转换器 (ViTs),在FER的特征学习方面显示出希望.
研究的目的:
- 为了引入一个新的重量受约束的ArcFace损失函数.
- 将这种损失函数集成到视觉变压器 (ViT) 框架中,以改进FER.
- 为了解决不平衡数据带来的偏差,并减少计算开销.
主要方法:
- 开发了一个重量受约束的ArcFace损失函数.
- 将拟议的损失集成到视觉转换器 (ViT) 架构中.
- 在RAF-DB和FER2013数据集上评估性能,与标准ArcFace和其他方法进行比较.
主要成果:
- 拟议的重量受约束的ArcFace损失显著改善了面部表情识别的准确性.
- 与标准的ArcFace相比,这种方法显示了更高的计算效率.
- 稳定重量优化导致减少计算开销和减轻数据分布偏差.
结论:
- 重量受约束的ArcFace损失为使用ViTs.Face的面部表情识别提供了更有效和高效的方法.
- 这种方法提高了对数据不平衡的模型稳定性,并提高了整体性能.
- 这些发现表明,在推进基于深度学习的FER系统方面,这是一个有前途的方向.
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