An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition


  • Cheng-Chieh Chiang Department of Information Technology, Takming University of Science and Technology, Taipei, Taiwan



ye Region Detection, Eye State Recognition, Local Binary Pattern, Support Vector Machine.


This paper proposes a framework to treat the eye information in a face image including the eye region detection and the eye state recognition. In order to make it possible to employ our approach to a real application in practice, our goal in this paper is to design not only a fast enough but also a high performance framework for the eye region detection and the eye state recognition. Our proposed framework mainly contains two parts: the first is to locate the eye regions in a face image, and the second is to recognize the states, either open or closed, of the eye regions. When a frame is captured from a video sequence, a face detection method is first performed to determine the positions of face regions. Next, face regions are converted into binary images and then we perform the horizontal and the vertical projections to locate the eye regions. Two visual features containing the intensity values of pixels and local binary pattern (LBP) are extracted from eye regions to classify the eye states with the support vector machine (SVM) approach.  This paper also demonstrates a several experiments to present the efficiency and effectiveness of our proposed framework. 


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How to Cite

Chiang, C.-C. (2014). An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition. European Journal of Applied Sciences, 2(5), 54–62.