Driver drowsiness detection in facial images

Abstract

Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, drowsy driver alert systems are meant to reduce the main cause of traffic accidents. Different approaches have been developed to tackle with the fatigue detection problem. Though most reliable techniques to asses fatigue involve the use of physical sensors to monitor drivers, they can be too intrusive and are less likely to be adopted by the car industry. A relatively new and effective trend consists on facial image analysis from video cameras that monitor drivers. How to extract effective features of fatigue from images is important for many image processing applications. This project proposes a face descriptor that can be used to detect driver fatigue in static frames. This descriptor represents each frame of a sequence as a pyramid of scaled images that are divided into non-overlapping blocks of equal size. The pyramid of images is combined with three different image descriptors. The final descriptors are filtered out using feature selection and a Support Vector Machine is used to predict the drowsiness state. The proposed method is tested on the public NTHUDDD dataset, which is the state-of-the-art dataset on driver drowsiness detection.