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Mathematical Problems in Engineering
Volume 2014, Article ID 860517, 10 pages
Research Article

Driver Fatigue Features Extraction

1Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2Engineering & Training Center, Nanjing University of Science and Technology, Room 403B, No. 200, Xiaolingwei Street, Nanjing 210094, China

Received 4 April 2014; Revised 26 May 2014; Accepted 26 May 2014; Published 22 June 2014

Academic Editor: Kalyana C. Veluvolu

Copyright © 2014 Gengtian Niu and Changming Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Driver fatigue is the main cause of traffic accidents. How to extract the effective features of fatigue is important for recognition accuracy and traffic safety. To solve the problem, this paper proposes a new method of driver fatigue features extraction based on the facial image sequence. In this method, first, each facial image in the sequence is divided into nonoverlapping blocks of the same size, and Gabor wavelets are employed to extract multiscale and multiorientation features. Then the mean value and standard deviation of each block’s features are calculated, respectively. Considering the facial performance of human fatigue is a dynamic process that developed over time, each block’s features are analyzed in the sequence. Finally, Adaboost algorithm is applied to select the most discriminating fatigue features. The proposed method was tested on a self-built database which includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method.