• Prasad Jadhav
• Harshal Chaudhari
• Rohit Pagar
• Pratik Nichite
• Vivek Waghmare

Drowsy driving has been identified as one of the leading causes of road accidents in recent years. This turns out to be a major issue for both the motorist and the people that utilize that road. Various accident prevention solutions are evolving as technology advances. The primary goal of avoiding road accidents can be accomplished by detecting a driver's tiredness in real time utilizing video recording with face detection. The alert sound will be activated after a camera has captured and detected drowsiness. The position of the head and the blinking of the eyes are used to determine whether or not the driver is drowsy. The camera detects real-time tiredness by detecting the face using a Local Binary Pattern and the eyes with a Haar Cascade Classifier. For eye blinking detection, a custom eye blinking file was created.

Alarm, convolutional neural network, drowsiness detection, eye detection, face detection, machine learning, supervised learning, unsupervised learning.

"A Review On Driver’s Drowsiness Detection Using Facial Feature", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c322-c326, May-2022, Available :http://www.jetir.org/papers/JETIR2205249.pdf

"A Review On Driver’s Drowsiness Detection Using Facial Feature", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc322-c326, May-2022, Available at : http://www.jetir.org/papers/JETIR2205249.pdf