Driving fatigue detection based on feature fusion of information entropy

Fatigue will affect the normal work and even cause accidents. In order to reduce fatigue's impact on people, we proposes a method for providing a real-time fatigued detection. Specifically, this method comprise the following steps. Firstly of all, we use Active Shape Model (ASM) to detect face, extract the Histogram of Orientation Gradient (HOG) features of eyes and mouth. Secondly, we use Support Vector Machine (SVM) to classify the states and Pose from Orthography and Scaling with Iterations (POSIT) algorithm to estimate the poses of the head. Thirdly, based on the states of face, we obtain a fatigue decision index, wherein a weight of the fatigue decision index is calculated by the Entropy-weighting method. Finally, we apply Bayesian method to evaluate driver's fatigued level based on calculated fatigue decision index. The final mean accuracy of this method is 83.3%.