A visual approach for driver inattention detection

• Published in: Pattern recognition Vol. 40; no. 8; pp. 2341 - 2355
• Main Authors: D’Orazio, T., Leo, M., Guaragnella, C., Distante, A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2007; Elsevier Science
• Subjects: Applied sciences; Artificial intelligence; Behavior analysis; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Image processing; Information, signal and communications theory; Iris detection; Mixture Gaussian model; Neural network; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Mixture Gaussian model; Behavior analysis; Neural network; Iris detection; Performance evaluation; Automatic classification; Probabilistic approach; Image processing; Mixture theory; Motor vehicle; Algorithm; Signal classification; Color image; Image segmentation; Gaussian process; Iris(eye); Image sequence; Automatic system; Robustness; Visual attention; Monitoring
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2007.01.018

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.