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Accurate detection of driver's eye state by computer vision is critical to driver drowsiness monitoring. The histogram of oriented gradients (HOG) is commonly used as descriptive feature of eye image for state classification. However, HOG often suffers from the limit of local gradient information. This paper proposes a new HOG-like feature of eye image, called cooccurrence matrix of oriented gradients (CMOG), for the purpose of more effectively classifying the eye state. By introducing the cooccurrence matrix, the CMOG enhances the ability of describing global gradient information of eye images. The ZJU eye blink database is used as the baseline images for performance comparison. The classification results show that the accuracy of CMOG reaches up to 95.9% in comparison with 91.9% by HOG under this database.