<![CDATA[This study evaluates and compares three widely used behavioral visual indicators for real-time fatigue detection to determine the most reliable decision metric for continuous monitoring applications. The indicators examined consist of the Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and Percentage of Eye Closure (PERCLOS), which represent micro-sleep events, yawning behavior, and cumulative eyelid closure, respectively. The experimental dataset includes 300 open–closed eye images, 300 yawning–non-yawning mouth images, and 20 five-minute video sequences to measure accumulated eye-closure over time. EAR obtained an accuracy of 89%, precision of 0.91, recall of 0.86, and an AUC of 0.93, showing high sensitivity to micro-sleep events lasting under two seconds. However, its performance decreased by up to 14% when the head pose shifted from a frontal orientation, indicating geometric dependency. MAR produced an average accuracy of 90%, precision of 0.87, recall of 0.84, and an AUC of 0.88, demonstrating strong responsiveness to yawning, with peak accuracy reaching 98%. Nevertheless, its stability dropped by 18% under tilted head orientation and lip movement variations, making it less reliable as a standalone metric for long-term monitoring. In contrast, PERCLOS achieved perfect performance with 100% accuracy, precision of 1.00, recall of 1.00, and an AUC of 1.00. Its robustness is attributed to cumulative eye-closure evaluation rather than geometric landmark measurement, allowing detection to remain unaffected by head pose variation. Statistical significance testing further revealed that PERCLOS outperformed both EAR and MAR (p 0.05), confirming its suitability as the dominant decision indicator. These findings establish PERCLOS as the most reliable metric for real-time visual fatigue assessment, while EAR and MAR serve as complementary indicators for detecting early fatigue symptoms such as micro-sleep and yawning behavior]]>
