# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 2 Background
With an SVM model obtaining an 87% accuracy, the paper [7] offers a real-time, non-invasive system leveraging impulse radio ultra-wideband (IR-UWB) radar to evaluate respiration rates. Both pieces draw attention to creative ways to track driver tiredness and help to lower the traffic congestion brought on by drowsiness. Similarly, another study [8] analyzed several sleepiness detection approaches, noting that many systems utilize software to monitor blink rates, eye closure, and yawning to efficiently alert drivers. While certain technologies can learn driver patterns and detect early signs of tiredness, their efficiency declines when drivers employ automatic lane-keeping systems. The study indicated that present technologies are generally trustworthy during the day but provide confusing results at night. An infrared camera was deployed to detect driver fatigue by measuring eye closure and yawning, with the goal of developing enhanced approaches for properly recognizing driver depletion in both daytime and nighttime settings. Combining federated and transfer learning to increase detection accuracy while protecting driver privacy by CKKS-based encryption, the paper [9] presents Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection (PFTL-DDD). Though the findings are not yet fit for real-world applications, the paper [10] presents an advanced driving assistance system (ADAS) employing deep learning with facial image sequences, attaining over a 65% accuracy and a 93% specificity with a fuzzy logic method. The research [11] shows a hybrid model using Multi-Task Cascaded Convolutional Neural Networks (MTCNNs) and Galvanic Skin Response (GSR) sensors that incorporates behavioral and physiological variables and achieves a 91% efficacy in identifying sleepiness changes. These findings, taken together, point to creative ideas meant to lower drowsiness-related accidents and improve driving safety. After reviewing the studies mentioned above, it was concluded that the eyes are of primary importance in systems designed to detect driver drowsiness. Thus, this study similarly emphasized the use of eyes as a key element in developing a drowsiness detection system. The researchers in this study employed a variety of machine learning and deep learning models to predict the level of drowsiness and then used a performance metric to evaluate how well the models performed [12; 13; 14; 15]. Several machine learning and deep learning models were used throughout this work and the performance of these models was evaluated using various performance metrics. The drowsy detection method involved extracting discrete features from images to detect eye and face regions. This identification process was facilitated by the use of various deep learning models including a CNN, VGG16, and MobileNet for actual sleepiness detection [16; 17; 18; 19; 20]. The major contributions of this study are as follows:  
* This study employs an efficient approach to drowsiness detection by analyzing not only facial expressions but also physiological indicators and driver behavior. * A comparative performance analysis of the proposed method with state-of-the-art deep learning models is demonstrated. * The MobileNet model presents its real-time processing capabilities and suitability for resource-constrained environments with the highest accuracy.