To deal with the insufficient competence for real-time detection and generalization of the existing methods for fatigue driving detection, this study proposes a detection method of eye closure features, which integrates the Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The CNN is employed to extract facial feature points and locate the eye Region Of Interest (ROI). Then the Histogram of Oriented Gradient (HOG) of the ROI serves as the feature classified by SVM to determine whether there exists the eye closure feature of fatigue driving in the original image. There into, graying and histogram equalization contribute to weakening the impact of illumination variation. The proposed method is implemented on both the PC platform and the ARM embedded platform, which is verified with regard to examinees subject to different levels of illumination. Experimental results prove that the method reaches an accuracy of above 94% for detecting eye closure features, with strong generalization and satisfied real-time reaction.