Eye state detection in facial image based on linear prediction error of wavelet coefficients

Eye state detection in facial image is a significant issue in face recognition, human-computer interface and driver fatigue monitoring system. In this paper, we first located the eye region in the upper area of the face region with AbaBoost algorithm. The linear predictor error distribution of wavelet coefficients was proposed as the statistics model to distinguish the eye states. We collected statistics (mean, variance, skewness, and kurtosis) of the prediction error distribution as eye state features. Build on these eye state features and support machine vector (SVM) with radial basis function (RBF) kernel a non-linear classifier is obtained by training samples of eye images. Experiment results with the classifier demonstrated that our method is an effective eye state detection approach which can satisfy various situations.