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- W4308702914 abstract "In recent years, driving fatigue for a long time has been a very dangerous behavior. People under the scenario of the need to stay awake for a long time, can grow over time and cumulative fatigue, drivers, in particular, this kind of distracting behavior is more dangerous, even threatened the life safety of the driver, the car have fatigue detection system has played a crucial role. In order to effectively remind drivers to avoid fatigue driving and reduce the number of car accidents, the system, based on pycharm environment, plans to use MTCNN face detection algorithm and ERT cascade regression algorithm to locate 68 key feature points of the face by calling the camera, and collect the features of previous faces<sup>[1]</sup>. Focus on monitoring the characteristic areas of eyes, nose and mouth, set a good threshold to judge the degree of fatigue state, using Open CV+Dlib these image processing library design logic algorithm to achieve the system program, through the face blink, yawn and nod times and relative to the threshold. This paper focuses on the design and implementation of fatigue detection system based on face facial feature recognition. The system uses Python+ Open CV to realize the simulation program and test, and uses the face detector of Dlib library to load the 68-point face facial feature recognition model. It can perform state analysis on behaviors such as yawning, eye closing, nodding and nodding, so as to judge whether the driver is tired or not, and use Flask data visualization and analysis<sup>[2]</sup>." @default.
- W4308702914 created "2022-11-14" @default.
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- W4308702914 date "2022-11-11" @default.
- W4308702914 modified "2023-10-03" @default.
- W4308702914 title "Research on fatigue detection system based on facial feature recognition in big data environment" @default.
- W4308702914 cites W3101998545 @default.
- W4308702914 doi "https://doi.org/10.1117/12.2641454" @default.
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