2025/08/01 by Tran Viet Khoa, Khoa, Tran Viet, Do Hai Son +7
Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Data modeling #Deep learning #FOS: Computer and information sciences #Feature extraction #Key (lock) #Robustness (evolution) #Sleep and Work-Related Fatigue #Software deployment
paper · pdf · doi:10.48550/arxiv.2508.00287
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Driver drowsiness is one of the main causes of road accidents and is recognized as a leading contributor to traffic-related fatalities. However, detecting drowsiness accurately remains a challenging task, especially in real-world settings where facial data from different individuals is decentralized and highly diverse. In this paper, we propose a novel framework for drowsiness detection that is designed to work effectively with heterogeneous and decentralized data. Our approach develops a new Spatial Self-Attention (SSA) mechanism integrated with a Long Short-Term Memory (LSTM) network to better extract key facial features and improve detection performance. To support federated learning, we employ a Gradient Similarity Comparison (GSC) that selects the most relevant trained models from different operators before aggregation. This improves the accuracy and robustness of the global model while preserving user privacy. We also develop a customized tool that automatically processes video data by extracting frames, detecting and cropping faces, and applying data augmentation techniques such as rotation, flipping, brightness adjustment, and zooming. Experimental results show that our framework achieves a detection accuracy of 89.9% in the federated learning settings, outperforming existing methods under various deployment scenarios. The results demonstrate the effectiveness of our approach in handling real-world data variability and highlight its potential for deployment in intelligent transportation systems to enhance road safety through early and reliable drowsiness detection.