合成表情比真实的表情更好,用于学习检测面部动作
Koichiro Niinuma1, Itir Onal Ertugrul2, Jeffrey F Cohn3
1Fujitsu Laboratories of America, Pittsburgh, PA, USA.
概括
与生成对抗网络 (GAN) 合成面部表情可以增强面部动作检测. 这种新的方法提高了对有限数据集的分类器性能,优于使用真实视频的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 培训面部动作分类器面临的挑战是由于小的注释视频数据集和不频繁的动作事件.
- 现有的方法在数据稀缺和不平衡的动作频率方面扎,限制了分类器的稳定性.
研究的目的:
- 开发和评估使用合成的面部表情数据进行面部动作检测的新方法.
- 克服面部表情识别中小型数据集和低动作频率的局限性.
主要方法:
- 从视频中重建3D面部形状,并将它们与正规视图对齐.
- 采用基于生成对抗网络 (GAN) 的模型来合成新的面部表情图像.
- 在合成和真实面部表情数据集上训练深度神经网络,以便进行比较分析.
主要成果:
- 在合成面部表情上训练的深度神经网络显著超过在未经改变的真实视频上训练的网络.
- 拟议的方法在面部动作检测准确度方面超过了当前最先进的方法.
- 合成的数据有效地增强了有限的现实世界面部表情数据集.
结论:
- 使用GANs生成面部表情,为面部动作检测中的数据稀缺提供了一个有希望的解决方案.
- 这种方法提高了分类器的性能和稳定性,为更准确的面部表情识别系统铺平了道路.
- 这项研究表明了合成数据生成在计算机视觉任务中推进机器学习的潜力.
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