走向使用OMG和机器学习进行面部表情识别的智能眼镜.
Ivana Kiprijanovska1, Simon Stankoski2, M John Broulidakis2
1Emteq Ltd., Brighton, BN1 9SB, UK. ivana.kiprijanovska@emteqlabs.com.
Scientific reports
|September 25, 2023
概括
新型智能眼镜使用光电学 (OMG) 准确检测面部表情,如微笑和眉. 头部运动不会显著影响检测,在现实世界模拟中达到93%的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 人与计算机的交互
- 可穿戴技术可穿戴技术
背景情况:
- 面部表情识别对于人机交互和情感计算至关重要.
- 现有的方法经常与现实世界条件 (如头部运动和不同表达强度) 斗争.
- 视镜学 (OMG) 为面部运动监测提供了一种潜在的非侵入性方法.
研究的目的:
- 评估OCOsense智能眼镜的有效性,使用光学成像 (OMG) 来监测和识别面部表情.
- 评估头部运动和表情变化对检测准确性的影响.
- 开发和验证机器学习模型,用于使用OMG数据实时识别面部表情.
主要方法:
- 27名年轻成年人进行了各种面部表情 (微笑,眉,眉毛抬起,眼) 控制强度,持续时间和头部运动.
- 配备OMG传感器的OCOsense智能眼镜捕获了面部肌肉活动.
- 一个机器学习模型在模拟的真实条件下被训练并使用leave-one-subject-out交叉验证进行评估.
主要成果:
- OCO传感器在捕捉明显的脸和眉毛运动方面表现出高精度和特异性.
- 头部运动并没有显著阻碍面部表情的检测.
- 机器学习模型在识别四个表达式和一个中立状态时,实现了93%的整体准确率 (0.90 f1-score).
结论:
- 基于光学成像的智能眼镜 (OCOsense) 提供了一种强大而准确的面部表情监测方法.
- 该系统对头部运动和表达强度的变化具有弹性,这表明其实际适用性.
- 这项技术有望在情感计算,辅助技术等领域应用.
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