Driver Fatigue Features Extraction

• Published in: Mathematical problems in engineering Vol. 2014; no. 1
• Main Authors: Niu, Gengtian, Wang, Changming
• Format: Journal Article
• Language: English
• Published: New York Hindawi Publishing Corporation 01.01.2014; John Wiley & Sons, Inc
• Subjects: Algorithms; Colleges & universities; Driver fatigue; Effectiveness; Facial; Fatigue; Fatigue (materials); Fatigue tests; Fatigue, Human; Feature extraction; Feature recognition; Human; Human performance; Kalman filters; Mathematical analysis; Methods; Morlet wavelet; Mouth; Product design; Traffic accidents; Traffic accidents & safety; Trucks
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2014/860517

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.