Driver fatigue monitoring system using video face images & physiological information

This paper is focused on driving impairment, due to fatigue of a driver. Both face features and physiological signal were used to monitor the fatigue. Analyzing the images from the video, we applied Active Shape Models (ASM) algorithm to get the changes on the face, such as degrees of eye closure, eye closure duration, blink frequency, degrees of mount opening, yawn duration. We analyzed Heart Rate Variability to evaluate the driver's level of fatigue using photoplethysmography signal. These parameters were combined using a fuzzy classifier to infer the level of driver fatigue. The system has been tested with different environment both night and day by simulation in the laboratory and with different users.