A Review of Face-based Fatigue and Drowsiness Detection Models
DOI:
https://doi.org/10.54388/jkues.v5i1.373Keywords:
fatigue detection, drowsiness detection, traditional machine learning models, deep learning models, hybrid modelsAbstract
Fatigue and drowsiness are critical issues affecting safety in various domains, including driving and industrial sectors. The ongoing progress in computing technology and artificial intelligence over recent years has facilitated advancements in fatigue detection systems. A multitude of experimental investigations have amassed empirical data and implemented diverse artificial intelligence algorithms alongside various feature combinations, with the intention of substantially improving the efficacy of these systems in real time applications. This paper aims to find effective models for fatigue and drowsiness detection based on face feature analysis. For this, comprehensively the latest models are examined, focusing on techniques, datasets, and performance metrics. The models are categorized into traditional machine learning, deep learning, and hybrid approaches, highlighting their strengths and limitations. This paper demonstrated that hybrid models emerged as the most effective and versatile solution for fatigue detection, offering superior accuracy, adaptability, and real-time performance across varied use cases.
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