Eye state detection is the process of determining whether the eyes are opened or closed. It has been widely employed in driver fatigue/drowsiness detection, human computer interaction. However, there are still lots of problems remaining unsolved such as free head movements, changing pose and illumination. Due to this, a new appearance based method is investigated in this work. Appearance based eigeneye features are extracted using Principal Component Analysis subspace method which is also a dimension and noise reduction method. And, for the first time in literature, eigeneyes are used for eye state detection in this work. K-nearest neighbor and multi-layer Perceptron Neural Networks with back-propagation learning algorithm are adopted to estimate eye state. Eigeneyes and Perceptron Neural Networks method pair obtained user-independent accuracy of 92.13%. Compared to previous works, the following two points are improved: (i) a new appearance based feature extraction method is proposed for eye state detection, and (ii) a faster approach is obtained for real-time systems while preserving accuracy substantially.