# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 2 Related Work
[10] further enhances real-time drowsiness detection with an impressive average accuracy of 97.23%. The model's architecture incorporates multiple convolutional and pooling layers, activation functions, and regularization techniques, contributing significantly to road safety technology by offering a highly accurate and efficient solution for real-time drowsiness detection. The integration of IoT and embedded systems has shown promising results including the paper by [PERSON] et al. [11] which combines EEG signals and eye blinks through a single prefrontal channel for reliable real-time detection. Utilizing a hybrid approach based on moving standard deviation (MSD) and discrete wavelet transform (DWT) for eye blink detection and filtering, the proposed method demonstrates reliability and suitability for real-time applications across diverse datasets. The study underscores the importance of adaptability and consistency in performance metrics, paving the way for future research directions to explore inter-subject variability and optimize classifier selection. The use of IoT technology is further exemplified by [PERSON] et al. [12], which proposes an ensemble deep learning architecture integrating eye and mouth subsamples for robust drowsiness detection. [PERSON] et al., in their paper [13], introduce an IoT-assisted framework combining computer vision and machine learning to monitor the driver's eye closure ratio. By integrating text-to-voice conversion and seat belt vibrators for alert notifications, the system demonstrates high accuracy in detecting driver fatigue across diverse scenarios, potentially saving lives and preventing accidents. Advanced algorithms and frameworks have further been developed and incorporated to enhance detection accuracy and efficiency. [PERSON] et al., in their paper [14], outline a comprehensive framework incorporating embedded systems, edge computing, cloud computing, and user interfaces for high-accuracy detection. Through rigorous testing and analysis, the system achieves high accuracy in detecting various driver behaviors, including yawning and closed eyes, with a 100% success rate in alert notifications. Computational performance testing on Raspberry Pi and Nvidia Jetson Nano showcases superior efficiency in the latter, highlighting the potential of GPU-accelerated edge computing for enhancing drowsiness detection systems. Future endeavors could explore hybrid approaches integrating physiological and visual cues to further enhance system robustness and performance. The study conducted by [PERSON] et al. [15] leverages the k-nearest neighbor (kNN) algorithm for real-time prediction of drowsiness based on eye behavior from live video streams. [PERSON] et al. [16] introduce a non-invasive multimodal machine learning approach, integrating environmental sensor data with human activity data for drowsiness detection. By integrating environmental sensor data with human activity data, the study demonstrates the potential of models trained on daily life data to predict a subject's drowsiness status during driving scenarios. Although modest, the findings underscore the value of leveraging large-scale behavioral data for improving drowsiness detection, highlighting the need for further investigation into lifestyle differences and factors influencing drowsiness. [PERSON] et al. [17] employ a Yolo v5-based method to enhance fatigue detection standards by categorizing facial expressions into distinct states to enhance traditional fatigue judgment standards. By considering both eye and mouth states, the method achieves higher accuracy and faster processing speeds compared to state-of-the-art methods, demonstrating practical applicability for improving traffic safety. In the paper by [PERSON] et al. [18], the algorithm proposed for blink-related feature extraction and electroencephalogram (EEG) signal enhancement demonstrates significant promise in improving driver drowsiness detection accuracy.