Accurate Fatigue Detection Based on Multiple Facial Morphological Features

Abstract

Fatigue driving is becoming a dangerous and common situation for drivers and represents a significant factor for fatal car crashes. Machine learning researchers utilized various sources of information to detect driver’s drowsiness. This study integrated the morphological features of both the eye and mouth regions and extensively investigated the fatigue detection problem from the aspects of feature numbers, classifiers, and modeling parameters. The proposed algorithm REcognizing the Drowsy Expression (REDE) achieved the 10-fold cross-validation accuracy 96.07% and took about 21 milliseconds to process one image. REDE outperformed the existing four studies on both fatigue detection accuracy and running time and is fast enough to handle the task of real-time fatigue monitoring captured at the rate of 30 frames per second. To further facilitate the research of fatigue detection, the raw data and the feature matrix were also released.