Facial Fatigue Detection Based on Machine Learning
One of the most important reasons for productivity decline and accidents is work fatigue. Work fatigue research has become more and more important in modern society. This paper proposes a method to detect fatigue, Build new features, propose new compensation methods, and combine the existing models to make the method adapt to the complex environment. As a result, it effectively improves the work fatigue detection efficiency and accuracy under the production environment.
KeywordsWork fatigue detection Features Compensation method
This project is supported by the Project of Intelligent Manufacturing Integrated Standardization and New Model Application.
- 1.Tao, Q., Ji, Y.: Review and comment on overwork in China. Human Res. Develop. China (2015)Google Scholar
- 2.Chen, J.W., Chun-Bo, B.I., Liao, H.J., et al.: Comparative research on measurement methods of work fatigue. J. Saf. Sci. Technol. (2011)Google Scholar
- 3.Azim, T., Jaffar, M.A., Mirza, A.M.: Fully automated real time fatigue detection of drivers through fuzzy expert systems. Elsevier Science Publishers B. V. (2014)Google Scholar
- 4.Yan, J.J., Kuo, H.H., Lin, Y.F., et al.: Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing. In: International Symposium on Computer Consumer and Control, pp. 243–246 (2016)Google Scholar
- 6.Niu, Q.N.: Research on fatigue driving detection method based on information fusion. Jilin University (2014)Google Scholar
- 7.Cootes, T.F., Taylor, C.J.: Constrained active appearance models. In: International Conference on Computer Vision, pp. 748–754 (2001)Google Scholar
- 8.Guo, Y.L.: Talking about the interpupillary distance. Metrol. Meas. Tech. (2014)Google Scholar