Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems

• A non-intrusive fatigue detection system based on the video analysis of drivers.
• Eye closure duration measured through eye state information and yawning analyzed through mouth state information.
• Lips are searched through spatial fuzzy c-means (s-FCM) clustering.
• Pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle.
• The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver.

This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through Viola–Jones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender. The system yielded an average accuracy of 100% on all the videos on which it was tested.