使用面部动作单元对面部的分裂面部计算机视觉/机器学习评估
John Wilson1, Joshua Rosenberg1, Mingyang L Gray1
1Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Facial plastic surgery & aesthetic medicine
|November 18, 2025
概括
一个自动化的视频分析管道使用半面镜像和面部行动单元 (AU) 量化准确预测面部的严重程度. 这种方法为主观的临床分级表提供了一个客观的替代方案,以改善患者管理和研究.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 临床医生分级尺度中的主观性和可变性限制了准确的面部评估.
- 需要客观和可重复的方法来管理患者和研究面部.
研究的目的:
- 评估一个自动化的视频分析管道来分类面部的严重程度.
- 通过半面镜像和面部行动单元 (AU) 量化对专家临床分级进行管道验证.
主要方法:
- 一个定制的Python管道处理了112个临床视频,生成了镜像半面结构.
- OpenFace 2.0提取了AU强度,XGBoost模型预测了每个面部区域的严重程度 (没有,不完整,完整).
- 模型经过训练,并根据多专家临床评估进行验证.
主要成果:
- 自动化管道在预测面部的严重程度方面取得了很高的准确性 (面部上部:0.83,面部中部:0.93,面部下部:0.84).
- 镜像AU强度显示了专家定义的严重程度组之间的显著差异,证实了特征的相关性.
- 权重的F1分数在0.82到0.92之间,表明了强的表现.
结论:
- 一个自动化管道利用半面镜像和镜像AU分析准确地从标准临床视频中预测面部的严重程度.
- 这项技术为面部的评估提供了一个有希望的客观工具,有可能改善临床决策和研究成果.
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