C. Padmaja et al., International Journal of Information Systems and Computer Sciences, 12(3), May - June 2023, 7 - 11 7 ABSTRACT Driving when fatigued is among the main causes of road deaths. Consequently, one ongoing research area is how to recognize driver fatigue and how to determine whether it is present. A large percentage of conventional methods are either based on machines, the behavior of people, or physiological processes. Some solutions need expensive sensors and data processing, while others are infiltrating and uncomfortable to the driver. As a consequence, this study creates an accurate, real-time method for identifying driver fatigue. The footage is captured by a camera, and image processing techniques are employed to recognize the driver's face in each frame. When facial landmarks on the detected face are pointed, the eye aspect ratio and mouth opening ratio are computed based on their values, and drowsiness is recognized utilizing generated adaptive thresholding. The following stage involves determining whether or not a discovered item is a face using SVM. It also checks the driver's eye aspect ratio (EAR) and mouth opening ratio (MOR) up to a predetermined number of times to look for signs of sleepiness and yawning. If sleepiness is identified, a warning email is sent to the registered email address. Key words: Mouth Aspect Ratio (MAR), Eye Aspect Ratio (EAR), Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM). 1. INTRODUCTION One of the leading factors in fatal car accidents is driving when tired. Long-distance or overnight bus drivers, long-haul truck drivers, particularly at night, and bus drivers are more prone to encounter this problem. A sleep-deprived driver is the greatest nightmare of every passenger worldwide. Auto accidents associated with exhaustion result in an important percentage of collisions and fatalities every year. Due to its significant practical significance, the detection of driver fatigue and its indication are currently a focus of research. Due to its significant practical significance, the detection of driver fatigue and its indication are currently a focus of research. The mechanism for detecting tiredness on a basic level. is composed of the acquisition system, the processing system, and the warning system. The acquisition system takes a video of the driver's frontal face at this point and transmits it to the processing block for online analysis to detect tiredness. If the warning system detects tiredness, it will issue the driver with a warning or alert. Physiological, behavioural, and vehicle-based tired driving detection methods make up the three primary categories. The vehicle-based system continuously monitors a wide range of variables, such as steering wheel movement, brake or accelerator use, vehicle speed, lateral acceleration, lane position deviations, etc. The identification of any abnormal change in these measures is AI-Powered Road Safety: Detecting Driver Fatigue through Visual Cues Dr C. Padmaja 1 , B. Nihalini Reddy 2 , M. Chandana 3 , T. Shreya 4 1 Assistant Professor, Dept. of ECE, G. Narayanamma Institute of Technology and Sciences (for women) Hyderabad, India, c.padmaja@gnits.ac.in 2 Dept. of ECE G. Narayanamma Institute of Technology and Sciences (for women) Hyderabad, India, chandanaprincy9@gmail.com 3 Dept. of ECE G. Narayanamma Institute of Technology and Sciences (for women) Hyderabad, India, nihalinibaddam@gmail.com 4 Dept. of ECE G. Narayanamma Institute of Technology and Sciences (for women) Hyderabad, India, shreyathokala60@gmail.com Received Date : April 03, 2023 Accepted Date : May 15, 2023 Published Date : June 07, 2023 ISSN 2319 – 7595 Volume 12, No.3, May - June 2023 International Journal of Information Systems and Computer Sciences Available Online at http://warse.org/IJISCS/static/pdf/file/ijiscs011232023.pdf https://doi.org/10.30534/ijiscs/2023/011232023