Driver fatigue recognition based on supervised LPP and MKSVM

Wei Huang; Wei Zhang

doi:10.1117/12.896510

8 July 2011 Driver fatigue recognition based on supervised LPP and MKSVM

Wei Huang; Wei Zhang

Author Affiliations +

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80091P (2011) https://doi.org/10.1117/12.896510
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

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.

Citation Download Citation

Wei Huang, Wei Huang, Wei Zhang, Wei Zhang, } "Driver fatigue recognition based on supervised LPP and MKSVM", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80091P (8 July 2011); doi: 10.1117/12.896510; https://doi.org/10.1117/12.896510

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