将GAN与反向相关性结合起来,构建个性化的面部表情.
Sen Yan1, Catherine Soladié1, Jean-Julien Aucouturier2
1CentraleSupelec, IETR, Rennes, France.
PloS one
|August 25, 2023
概括
本研究引入了一种新方法,将生成对抗网络 (GAN) 与反向相关性结合起来,在没有大型数据集的情况下创建个性化面部表情,包括复杂的情绪.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 情感计算是一种情感计算.
背景情况:
- 深度学习能够操纵面部表情,但缺乏个性化,并与非基本情绪作斗争.
- 目前的方法依赖于大型标记数据库,限制表达范围和复杂性.
- 现有的技术无法模拟微妙或个性化的情绪表达.
研究的目的:
- 开发一种新的,跨学科的方法来实现个性化面部表情的生成.
- 克服现有方法在处理非基本情绪和数据依赖方面的局限性.
- 为了能够对面部表情合成进行细粒度的控制.
主要方法:
- 结合生成对抗网络 (GAN) 与心理物理逆相关性.
- 使用反向相关性来提取面部表情的个性化心理表现.
- 开发一个不需要大型标记数据库或专家知识的系统.
主要成果:
- 成功生成基本和非基本情绪的个性化面部表情原型.
- 通过生成原型来操纵面部表情的能力.
- 引入了主导和互补的行动单元的概念来描述表达原型,挑战普遍性.
结论:
- 提出的跨学科方法有效地产生个性化的面部表情,包括复杂的情绪.
- 该方法克服了对大型数据集的依赖,并提供细粒度的控制.
- 这些发现表明一种新的方式来表示和生成面部表情,超越了普遍的原型.
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