Open and closed states of eyes play an important role in human-computer interaction. They can be used as communication method for people with severe disabilities providing an alternate input modality to control a computer or as detection method for a driver's drowsiness. This paper introduces a study on two eye components (i.e., iris and sclera) for robust open or closed eye classification. Evidently, the area of iris or sclera increases while a person opens an eye and decreases while an eye is closing. In particular, the distributions of these eye components, during each eye state, form a bell-like shape. Consequently, an eye state classification can be effectively achieved by Bayes classifier. Finally, the performance comparison of the proposed features against the ground truth is discussed.