# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 2 Related Work
By integrating blink-related features with filtered EEG band power, the algorithm achieves superior performance, emphasizing the importance of simultaneous feature extraction and elimination for robust classification. Future research may explore the algorithm's applicability across diverse cohorts and EEG-based monitoring applications, highlighting its potential to advance driver safety technology. The Two-Stream Spatial Temporal Graph Convolutional Networks (2s-STGCNs) framework by [PERSON] et al. [19] leverages spatial and temporal graph convolution networks for real-time facial landmark analysis, while the Deep Convolutional Model based on feature Recalibration and Fusion (RF-DCM) model in the paper by [PERSON] et al. [20] integrates multiple subnetworks for state-of-the-art accuracy in fatigue detection. [PERSON] et al. [21] introduce the Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection (PFTL-DDD) method, combining federated learning and transfer learning to improve classification performance and address privacy concerns by preserving driver data on edge nodes. Despite facing challenges with gradient descent efficiency and non-IID datasets, ongoing research focuses on enhancing convergence and optimizing global parameter aggregation algorithms, thereby promising further improvements in model accuracy and efficiency. [PERSON] et al. [22] present the Isotropic Self-Supervised Learning with momentum constant (IsoSSL-MoCo) strategy, which demonstrates remarkable effectiveness in enhancing the performance of fusion models for driver drowsiness detection. By optimizing MoCo's dictionary and leveraging multimodal features extracted from various scenarios, this strategy enhances generalization and enables accurate drowsiness detection. Future work aims to enhance generalization ability and interference resistance by increasing the complexity of samples for self-supervised learning, highlighting the continuous efforts to refine model performance and adaptability. [PERSON] et al. [23] investigate drowsiness detection using three traditional machine learning classifiers (Support Vector Machine, Naive Bayes, and Random Forest) and a deep neural network (VGG16), achieving the highest accuracy of 97.20% with the neural network. Unlike previous studies that used recorded EAR and MOR values, this study utilizes direct image pixels from facial geometry for classification, demonstrating improved accuracy and non-invasive, cost-effective methods suitable for automobile integration. Limitations include a constrained dataset size and the need for more diverse samples, suggesting future research should explore additional biometric signals and multimodal data to enhance robustness and generalization. There are revolutionary opportunities for data-driven, real-time road safety interventions when IoT and edge computing technologies are integrated with driver sleepiness detection. By utilizing edge computing in conjunction with IoT-enabled devices, traditional systems that rely on centralized processing can overcome their latency, reliability, and data privacy issues. The adaptive threshold method for drowsiness detection suggested in this study is optimized for direct processing on vehicle-embedded systems and tracks and evaluates important indicators like the mouth aperture ratio (MAR) and eye closure ratio (ECR). Because the driver's face data are kept within local processing units, the edge-based method greatly reduces data transfer to the cloud, leading to lower latency and improved data privacy. Furthermore, because our adaptive threshold approach maintains high accuracy while operating effectively on constrained hardware resources, it is naturally suited to IoT situations. The system provides scalable solutions that can be applied to a variety of IoT-enabled applications, ranging from individual car systems to extensive fleet management networks, by integrating edge devices, each of which is built with these computationally efficient, lightweight algorithms.