Abstract

Driver fatigue is one of the main reasons for traffic accidents. Face images include abundant fatigue information. Most driver fatigue detection methods extract local facial features. This paper proposes to extract global features to detect driver fatigue. Firstly global facial features are extracted by PCA. Secondly, weak classifiers are constructed by decision trees based on the PCA coefficients. Finally AdaBoost algorithm is applied to select the most critical features and build a strong classifier for fatigue detection. The method is validated on human subjects of different genders under real-life fatigue conditions. The test data includes thirty people's 4800 images with illumination and pose variations. In contrast, fatigue is also detected by LDA classifier and global features as a baseline. The average recognition rate of the proposed method with no more than 138 AdaBoosted global features is 80.19% which is bettor than 73.21% achieved by the baseline using 600 global features, and the computational cost of the proposed method is much lower.

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