Driver fatigue recognition based on supervised LPP and MKSVM

Driver fatigue is a significant factor in many traffic accidents. In this paper, a novel approach is proposed to recognize driver fatigue. First of all, in order to extract effective feature of fatigue expression from face images, supervised locality preserving projections (SLPP) is adopted, which can solve the problem that LPP ignores the within-class local structure by adopting prior class label information. And then multiple kernels support vector machines (MKSVM) is employed to recognizing fatigue expression, Compared to SVM, which can improve the interpretability of decision function and performance of fatigue recognition. Experimental results are shown to demonstrate the effectiveness of the proposed method.

[1]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[2]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Xuegang Wang,et al.  Supervised kernel neighborhood preserving projections for radar target recognition , 2008, Signal Process..

[4]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[5]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Xia Li-min,et al.  Fatigue recognition based on adaptive locality preserving projections , 2010 .

[7]  Luo Da-yong.  Fatigue Recognition Based on Facial Motion and Improved Locality Preserving Projections , 2010 .

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

[9]  Yongliang Xiao,et al.  Shot boundary Detection based on supervised locality preserving projections and KNN-SVM classifier , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[10]  Yongliang Xiao,et al.  Face recognition with supervised spectral regression and multiple kernel SVM , 2010, 2010 2nd International Conference on Advanced Computer Control.

[11]  Min Tan,et al.  Multiple kernel learning from sets of partially matching image features , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Pengfei Shi,et al.  Yawning detection for determining driver drowsiness , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..