Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors

Driving fatigue detection has been the subject of several studies which relied on a set of features describing driver’s facial expressions, driving behaviors, and bio-signals. The purpose of this study is to improve driving fatigue detection by identifying the adequate set of features which accurately characterize fatigued drivers. The considered features are derived from non-intrusive sensors; they are related to the changes in driving behavior and visual facial expressions. The relevance is first investigated by several feature selection methods. Second, a meta-analysis was performed to investigate method agreement about the relevance of each feature in driving fatigue recognition. Support vector machine and DBSCAN classifiers were used to detect fatigue by means of the identified features. Experimental analyses are performed on a real-world database, collected through the computer vision system “FaceLab” and car sensors, from 66 senior drivers when driving an instrumented car on a highway. Results provide a list of the most discriminative features, which enhances the classification average accuracy to 89.13%.