电子测量与仪器学报

颜文琴,郑秀娟,殷中平,张学刚,贾明,刘伯相,涂海燕.融合面部外观与生理表征的作业人员疲劳判别[J].电子测量与仪器学报,2025,39(10):12-21

融合面部外观与生理表征的作业人员疲劳判别

Fatigue identification of workers by integrating facialappearance and physiological characteristics

DOI：

英文关键词:fatigue detection video facial appearance physiological spatiotemporal map distraction-free

基金项目:

作者	单位
颜文琴	1.四川大学电气工程学院成都610065；2.四川大学信息与自动化技术四川省高校重点实验室成都610065
郑秀娟	1.四川大学电气工程学院成都610065；2.四川大学信息与自动化技术四川省高校重点实验室成都610065
殷中平	深圳中广核工程设计有限公司深圳518172
张学刚	中广核工程有限公司核电安全技术与装备全国重点实验室深圳518172
贾明	深圳中广核工程设计有限公司深圳518172
刘伯相	1.四川大学电气工程学院成都610065；2.四川大学信息与自动化技术四川省高校重点实验室成都610065
涂海燕	1.四川大学电气工程学院成都610065；2.四川大学信息与自动化技术四川省高校重点实验室成都610065

Author	Institution
Yan Wenqin	1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China；2. Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, China
Zheng Xiujuan	1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China；2. Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, China
Yin Zhongping	China Nuclear Power Design Co., Ltd (Shenzhen), Shenzhen 518172, China
Zhang Xuegang	State Key Laboratory of Nuclear Power Safety Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
Jia Ming	China Nuclear Power Design Co., Ltd (Shenzhen), Shenzhen 518172, China
Liu Boxiang	1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China；2. Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, China
Tu Haiyan	1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China；2. Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, China

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中文摘要:

在工业生产中，长时间和高强度的作业易导致人员疲劳，从而增加安全事故的风险。已有研究表明，接触式生理特征能有效表征疲劳状态，但在工业环境中采用接触式设备获取生理信号进行疲劳判别会干扰正常作业。因此，基于监控视频的疲劳判别成为更实际的选择，然而现有方法主要关注嘴部和眼部特征，未能全面反映疲劳状态。为此，提出了基于视频的融合面部外观与生理表征的无干扰式疲劳判别方法，通过双支路网络模型实现对作业人员疲劳判别。首先，在视频中定位面部感兴趣区域并进行子区域划分，通过提取皮肤反射光变化获取视频隐含的生理表征信息，进而构建生理时空图。接着，搭建双支路三维卷积网络分别提取面部外观和生理表征特征。最后，将两者特征融合并输入全连接层，以映射最终的疲劳判别结果。通过模拟工业生产任务获取的疲劳数据集验证了所提方法的性能。实验结果表明，基于视频的融合面部外观与生理表征的疲劳判别准确率达到88%，相较于现有技术具有更高的准确性和更强的现场适用性。

英文摘要:

In industrial production, prolonged and high-intensity operations can lead to worker fatigue, increasing the risk of safety incidents. Existing research has shown that contact-based physiological features can effectively represent fatigue status, but using contact-based instruments to monitor fatigue in industrial environments interferes with operations. Therefore, fatigue detection based on surveillance video has become a more practical choice. Current methods mainly focus on mouth and eye features, failing to comprehensively reflect fatigue status. To address this issue, we propose a non-intrusive fatigue detection method that integrates facial appearance and physiological representation, utilizing a video-based dual-branch network model for monitoring worker fatigue. First, we locate the facial areas of interest in the video and segment these areas. By extracting changes in skin reflectance due to variations in capillary blood volume, we construct a physiological spatiotemporal map. Next, we build a dual-branch 3D convolutional network to extract facial appearance and physiological feature representations separately. Finally, we fuse these features and input them into a fully connected layer to map the final fatigue detection results. The proposed method is validated using a fatigue dataset obtained from simulated industrial production tasks. Experimental results demonstrate that the fatigue detection accuracy, based on the integration of facial appearance and physiological features from video, reaches 88%, offering higher accuracy and stronger applicability in industrial settings compared to existing technologies.

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