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首页> 外文会议>International Conference on Applications and Techniques in Cyber Intelligence >Face Fatigue Detection Method Based on MTCNN and Machine Vision
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Face Fatigue Detection Method Based on MTCNN and Machine Vision

机译：基于MTCNN和机器视觉的面部疲劳检测方法

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摘要

Multi-task Cascaded Convolutional Networks (MTCNN) is a face detection method based on deep learning. Compared with the traditional parametric model and regression-based method, MTCNN is more robust to light, angle and facial expression changes in the natural environment, while machine vision as an important branch of the current artificial intelligence technology, it realizes the visual function of the human eye through a computer. By combining MTCNN with machine vision, real-time face detection can be realized. This detection method can be used for in the fields of identification, face behavior analysis, etc. it is especially suitable for the field of fatigue detection and early warning for long-term computer use. Based on this, this paper proposes a face fatigue detection method based on MTCNN and machine vision. This method combines three parameters of blink frequency, yawn frequency and drowsiness frequency, and uses fuzzy neural network to force computer users to know their fatigue in time. For office workers, it can improve their work efficiency and even prevent the occurrence of accidents. For ordinary users, it can remind them to protect their eyes and help them maintain body-health. With the promotion of this method, people can realize the danger of fatigue from the source and develop a healthy way of life and work.
机译：多任务级联卷积网络（MTCNN）是基于深度学习的面部检测方法。与传统的参数模型和基于回归的方法相比，MTCNN对自然环境中的光，角度和面部表情变化更加稳健，而机器视觉作为当前人工智能技术的重要分支，它实现了视觉功能人眼通过计算机。通过将MTCNN与机器视觉结合，可以实现实时脸部检测。该检测方法可用于识别，面部行为分析等领域。它特别适用于疲劳检测领域和长期计算机使用的预警领域。基于此，本文提出了一种基于MTCNN和机器视觉的面疲劳检测方法。该方法结合了闪烁频率，打开频率和嗜睡频率的三个参数，并使用模糊神经网络强制计算机用户及时了解它们的疲劳。对于办公室工作人员来说，它可以提高他们的工作效率，甚至可以防止发生事故的发生。对于普通用户来说，它可以提醒他们保护他们的眼睛并帮助他们保持身体健康。随着这种方法的推广，人们可以实现源头疲劳的危险，并培养一种健康的生命方式。

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