Driver Drowsiness Detection Based on Novel Eye Openness Recognition Method and Unsupervised Feature Learning

In this paper, we proposed a driver drowsiness detection method for which only eyelid movement information was required. The proposed method consists of two major parts. 1) In order to obtain accurate eye openness estimation, a vision based eye openness recognition method was proposed to obtain an regression model that directly gave degree of eye openness from a low-resolution eye image without complex geometry modeling, which is efficient and robust to degraded image quality. 2) A novel feature extraction method based on unsupervised learning was also proposed to reveal hidden pattern from eyelid movements as well as reduce the feature dimension. The proposed method was evaluated and shown good performance.

[1]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[2]  Yoshihiro Noguchi,et al.  Driver-Independent Assessment of Arousal States from Video Sequences Based on the Classification of Eyeblink Patterns , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[3]  Yun Fu,et al.  Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression , 2008, IEEE Transactions on Image Processing.

[4]  Haruki Kawanaka,et al.  Prediction of the time when a driver reaches critical drowsiness level based on driver monitoring before and while driving , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[5]  Chin-Teng Lin,et al.  Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[6]  Aurobinda Routray,et al.  A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers , 2013, IEEE Transactions on Intelligent Transportation Systems.

[7]  Peter Rossiter,et al.  Applying neural network analysis on heart rate variability data to assess driver fatigue , 2011, Expert Syst. Appl..

[8]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT , 1994 .

[9]  Miguel Ángel Sotelo,et al.  Real-time system for monitoring driver vigilance , 2004, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[10]  Vitomir Struc,et al.  Photometric Normalization Techniques for Illumination Invariance , 2011 .

[11]  Walid Mahdi,et al.  Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration , 2014, Machine Vision and Applications.

[12]  Haitao Wang,et al.  Face recognition under varying lighting conditions using self quotient image , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[13]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Kenneth Sundaraj,et al.  Detecting Driver Drowsiness Based on Sensors: A Review , 2012, Sensors.

[15]  Bin Yang,et al.  Camera-based drowsiness reference for driver state classification under real driving conditions , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  B. Tefft,et al.  Prevalence of motor vehicle crashes involving drowsy drivers, United States, 1999-2008. , 2012, Accident; analysis and prevention.

[17]  Simon G Hosking,et al.  Predicting driver drowsiness using vehicle measures: recent insights and future challenges. , 2009, Journal of safety research.

[18]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Zheng Ming Xu,et al.  Methodology and initial analysis results for development of non-invasive and hybrid driver drowsiness detection systems , 2007, The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007).