Driver Fatigue Detection by Fusing Multiple Cues

A video-based driver fatigue detection system is presented. The system automatically locates the face in the first frame, and then tracks the eyes in subsequent frames. Four cues which characterises fatigue are used to determine the fatigue level. We used Support Vector Machines to estimate the percentage eye closure, which is the strongest cue. Improved results were achieved by using Support Vector Machines in comparison to Naive Bayes classifier. The performance was further improved by fusing all four cues using fuzzy rules.

[1]  Saman K. Halgamuge,et al.  Self-evolving neural networks for rule-based data processing , 1997, IEEE Trans. Signal Process..

[2]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[3]  LUIGI STRINGA,et al.  Eyes detection for face recognition , 1993, Appl. Artif. Intell..

[4]  Saman K. Halgamuge,et al.  Optimal Weighting of Landmarks for Face Recognition , 2006, J. Multim..

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Manfred Glesner,et al.  An alternative approach for generation of membership functions and fuzzy rules based on radial and cubic basis function networks , 1995, Int. J. Approx. Reason..

[7]  Shumeet Baluja.  Using Labeled and Unlabeled Data for Probabilistic Modeling of Face Orientation , 2000, Int. J. Pattern Recognit. Artif. Intell..

[8]  Saman K. Halgamuge,et al.  Optimised landmark model matching for face recognition , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  A. Craig,et al.  Driver fatigue: electroencephalography and psychological assessment. , 2002, Psychophysiology.

[10]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[11]  Saman K. Halgamuge,et al.  A trainable transparent universal approximator for defuzzification in Mamdani-type neuro-fuzzy controllers , 1998, IEEE Trans. Fuzzy Syst..

[12]  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..

[13]  J Rogé,et al.  Variations of the level of vigilance and of behavioural activities during simulated automobile driving. , 2001, Accident; analysis and prevention.

[14]  Mubarak Shah,et al.  Determining driver visual attention with one camera , 2003, IEEE Trans. Intell. Transp. Syst..

[15]  Jean-Christophe Popieul,et al.  Using driver's head movements evolution as a drowsiness indicator , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Manfred Glesner,et al.  Fuzzy neural networks: between functional equivalence and applicability , 1995, Int. J. Neural Syst..

[18]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[19]  Tim Horberry,et al.  REVIEW OF FATIGUE DETECTION AND PREDICTION TECHNOLOGIES , 2000 .