## 2 Related Work
### Existing Datasets  
This section outlines several widely utilized datasets designed specifically for the detection of fatigue and drowsiness. Our research primarily concentrates on video datasets, which do not contain data derived from physical sensors, and the videos are captured by using ordinary cameras. The UTA-RLDD dataset [32] comprises approximately 30 h of RGB videos featuring 60 healthy participants from diverse ethnic backgrounds, with varying ages ranging from 20 to 59 years old. Each participant contributed 1 video for each of three distinct states, i.e., alertness, low vigilance, and drowsiness, totaling 180 videos. Recordings were captured from diverse angles in real-life environments against different backgrounds. A notable aspect of this dataset is the authenticity of drowsiness samples, which contrast with staged portrayals found in other datasets. Moreover, data collection involved the use of different cameras, with each participant recording themselves in a real-life indoor setting of their choosing by using either a cell phone or a web camera. The YawDD dataset [23] comprises two publicly available sub-datasets featuring RGB videos. Participants in these videos engage in simulated driving scenarios, while the vehicle remains stationary. In the first sub-dataset, a camera is positioned beneath the front mirror of the car, capturing three to four videos per participant. This sub-dataset offers a total of 322 videos depicting drivers (57 males and 50 females), with and without glasses/sunglasses, representing diverse ethnic backgrounds. In the second sub-dataset, which includes 29 videos, the camera is mounted on the car's dashboard in front of the driver. Each video showcases various mouth conditions, including normal, talking/singing, and yawning. However, some individuals in the YawDD dataset videos simulate yawning rather than naturally exhibiting the behavior. Hence, the authors of [22] decided to collect their own dataset to capture more authentic fatigue. Ten volunteers each participated in the creation of two videos: one depicting normal behavior and the other featuring signs of fatigue, including moments of eye closure, conversation, laughter, and yawning. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Used Features** & \
\begin{tabular}{c} **Technique Used for** \\ **Fatigue Detection** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy of Fatigue** \\ **Detection Models** \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. [27]} & Eyebrows, mouth contours, head & \multirow{2}{*}{Static tests} & Significant differences in these metrics between high \\  & movements, eye region, and facial area & & and low fatigue states \\ \hline [PERSON] et al. [28] & Eye-tracking data & Machine learning techniques & 85\% \\ \hline [PERSON] et al. [29] & EEG data collected from sensor & Deep learning techniques & 99.941\% \\ \hline \multirow{2}{*}{[PERSON] et al. [30]} & EEG and EDA data collected from sensors & \multirow{2}{*}{Machine learning techniques} & \multirow{2}{*}{96.2\%} \\  & and facial features obtained from videos & & \\ \hline [PERSON] et al. [31] & Heart rate and respiration signals & Machine learning techniques & 92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._dataset aims to mitigate discrepancies in experimental outcomes arising from variations in skin color. The NTHU-DDD dataset [33] consists of 360 (infrared (IR) and RGB) videos of 36 subjects showcasing a range of simulated driving situations, including regular driving, yawning, slow blinking, nodding off, and laughing. These recordings capture subjects under both daytime and nighttime lighting conditions. They were directed by an experimenter to perform a sequence of eight actions across five different scenarios: BareFace, Glasses, Sunglasses, Night-BareFace, and Night-Glasses. The authors of [34] created the Licensed Crane Operators dataset, which features videos obtained through interviews conducted by the authors with five skilled crane operators.