# Driver Identification and Detection of Drowsiness while Driving
## 2 Related Work  
Innovative strategies for enhancing vehicle security and deterring theft have been a focus of recent research. A notable development in this area can be found within [4], which introduces a system based on facial recognition to safeguard against vehicle theft, effectively employing global systems for mobile communications (GSMs) and Arduino [5] technologies. Additionally, the authors of [6] propose a vehicle tracking system that integrates GSM communication with global positioning system (GPS) technology, enabling real-time monitoring through short message service (SMS). The paper [7] aims to detect driver fatigue by analyzing vehicle operation data, proposing a system using machine learning to distinguish between fatigued and non-fatigued drivers based on driving behavior. The study [8] explores the early detection of driver drowsiness using ensemble machine learning, combining multiple sensors. It proposes a detection system merging physiological  
Figure 1: System operation for the two use cases. (e.g., heart rate and electromyographic activity) and vehicle sensors (e.g., steering angle and acceleration). These methodologies underscore the importance of integrating advanced technological systems to improve vehicle security, marking a significant stride in the use of novel technologies to prevent car theft and provide both efficient and accessible solutions. In the scientific literature, numerous diverse approaches addressing road safety, particularly driver behavior, using AI techniques can be found. For instance, the authors of [9] analyzed automated systems that identify improper human driving behavior and proposed a self-adaptation model for trust management based on deep learning, which first identifies safe and unsafe drivers and then classifies the behaviors of safe drivers using deep learning. The survey in [10] covers different studies that employed at least one camera to observe a driver inside a vehicle. In fact, one of the most difficult issues related to the use of video devices in automotive applications is the need for a high frame rate. In this sense, the authors of [11] proposed a preliminary version of a motion magnification method that addressed this issue. Moreover, several very recent bibliographic references focus on the application of different AI models based on machine learning (ML) to detect driver drowsiness. One of the first reviews of the literature on driver drowsiness detection based on behavioral measures using ML techniques is [12]. Stacked deep convolutional neural networks (CNNs) were used in [13] to detect faces and extract the eye region from facial images to classify the driver as sleep or non-sleep. The authors of [14] presented a driver drowsiness-detection model based on deep learning techniques for the brain-computer interface. The authors of [15] also suggested the use of a CNN technique as the best machine learning algorithm to detect microsleep and drowsiness. Additionally, the authors of [16] proposed another real-time driver drowsiness-detection system based on the area of eye closure and the use of a CNN [17]. The authors of [18] used a public dataset to propose an architecture that detects driver drowsiness by comprising four deep learning models to extract four different types of features. The authors of [19] analyzed driver behavior and detected signs of drowsiness using a variety of sensor data, such as cameras, motion sensors, and physiological data, to train deep learning models. A different approach based on the study of emotions through facial images using CNN to extract relevant emotional features is followed in [20]. A modified deep learning architecture proposing specific adaptations in the neural network was used in [21] to analyze and process relevant data, such as driver images and driving patterns.