Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning

TL;DR

This paper introduces a privacy-preserving driver drowsiness detection framework using spatial self-attention and federated learning, achieving high accuracy with decentralized facial data while maintaining user privacy.

Contribution

It presents a novel SSA-LSTM model combined with GSC for effective federated learning-based drowsiness detection on decentralized data.

Findings

Achieved 89.9% detection accuracy in federated settings

Outperformed existing methods in real-world scenarios

Enhanced robustness and privacy preservation

Abstract

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.