Drowsiness is a major concern with respect to road safety. Drivers’ unconsciousness due to microsleep can frequently lead to destructive accidents. Falling asleep at the wheel is usually related to lack of sleep, exhaustion, or mental health problems. In the UAE, the ministry of interior recorded 2931 car crashes in 2020. The number increased in 2021 to 3488 records. The majority of these traffic accidents were caused by distracted driving due to drowsiness, sudden swerving, or failure to maintain a safe distance between vehicles 
. In this situation, it is crucial to exploit new technologies to plan and design systems that can track drivers and estimate their level of attention while driving. As multiple countries are concerned regarding this issue, researchers worldwide worked on building Driver Drowsiness Detection (DDD) systems that are capable of detecting drivers’ drowsiness signs in the early stages.
According to the literature, drowsiness detection systems can be grouped into three categories based on the measures that are used to detect the drowsiness signs 
: biological-based, vehicle-based, and image-based systems. In the first category, biological-based measures rely on monitoring the body’s physiological signals including, ElectroEncephaloGraphy (EEG), ElectroCardioGraphy (ECG), ElectroMyoGraphy (EMG), Electro-OculoGraphy (EOG) signals, and blood pressure 
. In this type of system, drowsiness is determined by detecting the signal’s deviation from the standard state’s characteristics and analyzing if the new signal indicates drowsiness. In the second category, vehicle-based measures depend on monitoring variations in the car’s movement patterns through different sensors’ installed to measure various vehicle and street parameters. To infer the drowsiness level, vehicle-based systems analyze the changes or abnormal behavior of the car, including, for example, the steering wheel angle, speed, or deviation from the lane 
. The third category is the image-based measures which depend mainly on the drowsiness signs that appear on the driver’s face and head. These systems detect drowsiness by monitoring the drivers’ head movements and facial parameters such as the eyes, mouth facial expressions, eyebrows, or respiration 
All three categories have some limitations 
. Biological-based systems can detect drowsiness in the initial stages due to their ability to compare the continuous changes in the physiological signals, but, in most biological-based systems, it is demanded that electrodes be connected to the driver’s body. This setup is usually inconvenient and uncomfortable for the driver. It also involves noise that affects the signal quality, leading to decreased accuracy. Vehicle-based systems depend generally on vehicle types, and can greatly be affected by multiple factors, including road characteristics, climate conditions, and the driver’s experience, habits, and ability to drive. Limitations of the image-based systems are strictly related to the quality of the camera used and its adaptability to different lighting conditions. The existence of objects covering parts of the face, such as glasses, sunglasses, masks, etc., can also affect the accuracy of image-based DDD systems. However, among these three systems, image-based systems are considered to be fully non-invasive, low cost, and minimally affected by road conditions. Therefore, image-based measures are widely deployed to develop versatile, affordable, real-time and, fully portable DDD devices 
In this work, we present a new image-based DDD system. It uses a unique combination of features derived from the driver’s facial parameters to train and test three classifiers, namely Random Forest (RF), sequential Neural Networks (NN), and linear Support Vector Machine (SVM). The features used in this system are Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head pose estimation. The proposed system is convenient for the driver in the sense that it does not require any sensors or equipment to be attached to the driver’s body. It is adaptable to be used in different vehicles, including buses, cars, motorcycles, and others. Evaluations of the proposed system on the National Tsing Hua University DDD (NTHUDDD) video dataset show that it can achieve accuracy up to 99%, indicating that it is an effective solution.