A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences

Fatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algorithm that utilizes facial multi-feature fusion, thoroughly considering the driver’s individual characteristics. To improve the judging accuracy of the driver’s facial expressions, a personalized threshold is proposed based on the normalization of the driver’s eyes and mouth opening and closing instead of the traditional average threshold, as individual drivers have different eye and mouth sizes. Given the dynamic changes in fatigue level, a sliding window model is designed for further calculating blinking duration ratio (BF), yawning frequency (YF), and nodding frequency (NF), and these evaluation indexes are used in the feature fusion model. The reliability of the algorithm is verified by the actual test results, which show that the detection accuracy reaches 95.6% and shows good application potential in fatigue detection applications. In this way, facial multi-feature fusion and fully considering the driver’s individual characteristics makes fatigue driving detection more accurate.