超越FACS:数据驱动的面部表情词典,用于预测自闭症
Evangelos Sariyanidi1, Lisa Yankowitz1, Robert T Schultz1,2
1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
概括
一个新的面部基础系统为分析面部运动提供了面部行动编码系统 (FACS) 的全面替代方案. 这种无监督的方法能够准确地捕捉所有面部表情,
科学领域:
- 计算机视觉
- 行为科学
- 机器学习
背景情况:
- 面部行动编码系统 (FACS) 对于将面部行为与心理健康联系起来至关重要,
- 自动化行动单元 (AU) 检测在准确性和覆盖性方面存在局限性,阻碍了全面的面部表情分析.
- 现有的方法难以代表面部表情的完整性,
研究的目的:
- 介绍面部基础,一个新的,数据驱动的编码系统面部运动分析.
- 克服自动化FACS编码的局限性,包括手动注释,有限的移动目录和非添加单元组合.
- 提供一个全面和无监督的方法来解构视频中的面部表情.
主要方法:
- 开发了一个数据驱动的编码系统,面部基础,单位代表本地化,可解释的3D面部运动.
- 实施了无监督学习框架, 消除了手动注释的需要.
- 确保面部基础重建所有可观测的面部运动,并且其单位是附加的.
主要成果:
- 面部基础准确地重建所有可观测的面部运动,与使用有限的AU的自动化FACS不同.
- 该系统没有监督,因此无需昂贵且耗时的手动注释.
- 面基单位是附加的,克服现有方法中非附加的AU组合的局限性.
- 在视频数据中预测自闭症诊断的表现优于领先的AU检测器.
结论:
- 面部基础作为第一个FACS替代方案, 将面部表情解构为视频中的局部动作.
- 面部基础的综合性和无监督性使其成为行为研究的宝贵工具.
- 这种方法有助于我们更好地理解面部表情与自闭症等精神疾病相关.
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