Driver fatigue detection based on deeply-learned facial expression representation

Abstract

Driver fatigue detection is a significant application in smart cars. In order to improve the accuracy and timeliness of driver fatigue detection, a fatigue detection algorithm based on deeply-learned facial expression analysis is proposed. Specifically, the face key point detection model is first trained by multi block local binary patterns (MB-LBP) and Adaboost classifier. Subsequently, the eyes and mouth state are detected by using the trained model to detect the 24 facial features. Afterwards, we calculate the number of two parameters that can describe the driver's fatigue state and the proportion of the closed eye time within the unit time (PERCLOS) and yawning frequency. Finally, the fuzzy inference system is utilized to deduce the driver's fatigue state (normal, slight fatigue, severe fatigue). Experimental results show that the proposed algorithm can detect driver fatigue degree quickly and accurately.