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退役的时间 F1-对动作单元检测的二进制得分.
Saurabh Hinduja1, Tara Nourivandi2, Jeffrey F Cohn1
1Department of Psychology, University of Pittsburgh, Pittsburgh, USA.
Pattern recognition letters
|August 1, 2024
概括
由于类不平衡,F1二进制分数对于评估行动单元检测是不可靠的. 研究人员建议将其替换为F1-微分数,以获得更准确的面部表情分析.
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:
- 行动单元检测对于面部表情识别至关重要,因为表情可以被分解成个别的行动单元.
- F1二元分数通常用于评估行动单元检测模型.
- 阶级不平衡在机器学习任务中构成了重大挑战,包括面部分析.
研究的目的:
- 反对使用F1二进制分数来评估行动单元检测模型.
- 为了证明阶级失衡对F1二进制分数可靠性的负面影响.
- 提出并证明F1微分作为更合适的替代度量.
主要方法:
- 研究了阶级不平衡对行动单元检测性能的影响.
- 评估了阶级不平衡在培训集,测试集以及对新数据的概括性的影响.
- 在不平衡条件下进行实证分析,以比较F1二元和F1微分的表现.
主要成果:
- 阶级不平衡大大削弱了F1二元分数在行动单元检测中的可靠性.
- 在面部分析中常见的不平衡数据集处理时,F1二进制分数提供了一个误导性的评估.
- 经验证据支持F1-微分数在准确反映模型性能方面的优越性.
结论:
- 由于其易受类不平衡的影响,F1二进制分数应该被取消为行动单元检测的评估指标.
- F1微分是评估行动单元检测模型的更强大和更可靠的指标,特别是在存在类不平衡的情况下.
- 采用F1-micro将导致更准确的评估和面部表情识别系统的进步.
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