Design and Implementation of a Driver’s Eye State Recognition Algorithm Based on PERCLOS

In order to improve the general detection accuracy of eye state, this paper puts forward an innovative method for judging human eye state based on PERCLOS. After pretreatment of the eye image, Hough transformation is used for ellipse detection and pupil position. The gray projection variance threshold analysis is then used to help make the final detection. Freeman chain and the Snake model algorithm are used for the corner detection and precise calculation of the height of an open eye. Thus the PERCLOS value and the eye state can be figured out. The performance of our eye state recognition algorithm is validated by more than 1000 images within product database. The statistics result shows that the fatigue detection accuracy rate can meet the need of usage in complex environment.