Focusing on the issue that the eye localization accuracy drastically affects the correct recognition rate of the eye state, an eye state recognition algorithm combined with online skin feature model was proposed. Firstly, an online skin model was established by fusing the Active Appearance Model (AAM) of the received face image and the skin characteristics of the active user. Secondly, in the preliminary positioned eye area, the online skin model was used again to calculate the precise location of the inner and outer corners of the eyes, and the optimal eye positions were computed by reference of the eye corners. Finally, the Local Binary Pattern (LBP) in the eye area was extracted, and the close and open state of the eyes was recognized effectively based on the Support Vector Machine (SVM). In the comparison experiments with eye corners location algorithm of global localization, the location error was further reduced, and in a low resolution face image, the average recognition accuracy of open eye state and close eye state were 95.03% and 95.47% respectively. Compared with the algorithms based on Haar features and Gabor features, the efficiency increased by 2.9% and 4.8% respectively. The theoretical analysis and simulation results show that the algorithm based on online feature can effectively improve the recognition efficiency of the eye state from real-time face video.