使用机器学习与面部表情和脉率检测欺骗
Kento Tsuchiya1, Ryo Hatano1, Hiroyuki Nishiyama1
1Department of Industrial Administration, Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki Noda, Chiba Japan.
Artificial life and robotics
|June 26, 2023
概括
本研究引入了一种机器学习方法,通过分析面部表情和脉率来检测远程采访中的欺骗行为. 该方法实现了高准确度,帮助面试官识别不真实的答案.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 人与计算机的交互
背景情况:
- 由于COVID-19大流行,远程面试很普遍,增加了对可靠欺骗检测的需求.
- 目前在面试中检测欺骗的方法严重依赖于面试人员的经验,并且不是自动化的.
- 面试对象可能很难传达真实性,或者在远程互动期间可能会尝试欺骗.
研究的目的:
- 开发一种机器学习方法,用于在远程采访中自动检测欺骗.
- 将面部表情分析与脉冲率数据集成在一起,以提高检测准确度.
- 创建一个现实的数据集,用于训练和评估欺骗检测模型.
主要方法:
- 开发了一个机器学习模型,以将面部表情特征与脉冲率数据相关联.
- 通过摄像头和智能手表记录的受试者的自然即兴反应创建了一个新的数据集.
- 该模型使用10倍交叉验证与随机森林分类器进行了评估.
主要成果:
- 拟议的机器学习方法表现出高精度,F1分数在0.75到0.88.8之间.
- 功能重要性分析揭示了对象特定的欺骗指标.
- 该模型有效地结合了面部和生理数据,用于欺骗检测.
结论:
- 该研究成功开发并验证了一种机器学习系统,用于在远程采访中检测欺骗.
- 这些发现突显了整合多式联络数据 (面部表情,脉率) 实现自动谎言检测的潜力.
- 个性化特征分析为不同受试者提供了对欺骗独特的生理和行为标记的见解.
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