Fatigue Detection from Facial Features Using AI to Prevent Accidents

Authors

  • 1Dr. P. P. Sadhu Naik, 2K. Uma Maheswari,3C. Indhu,4A. Rajitha,5D. Triveni,6 J. Susmitha Author

DOI:

https://doi.org/10.64751/

Abstract

One of the main causes of injuries and fatalities worldwide is road accidents brought on by fatigued drivers, particularly when driving long distances and at night. A mouth aspect ratio (MAR)-based deep CNN model is suggested to enhance detection. The probability of such collisions is greatly decreased by the system's constant monitoring and alerting of the driver. The system analyses fatigue levels using ml and dl models. For precise facial landmark detection, the DLIB library is utilised. This technology can be installed in vehicles as an inexpensive driver support system and promises to improve road safety and lower accident rates. Keywords: Face Recognition, Python flask, OpenCV, DLIB, Facial Landmark Analysis, Real-time Alert System.

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Published

2026-07-31

Issue

Vol. 5 No. 3 (2026): Volume 5, Issue 3, 2026

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

1Dr. P. P. Sadhu Naik, 2K. Uma Maheswari,3C. Indhu,4A. Rajitha,5D. Triveni,6 J. Susmitha. (2026). Fatigue Detection from Facial Features Using AI to Prevent Accidents. International Journal of Data Science and IoT Management System, 5(3), 651-658. https://doi.org/10.64751/