Detection and classification of eye state in IR camera for driver drowsiness identification

An eye detection and eye state (open/close) classification methodology for driver drowsiness identification using IR camera has been presented in this paper. In this proposed methodology, otsu thresholding is used to extract face region. Eye localization is done by locating facial landmarks such as eyebrow and possible face center. Morphological operation and K-means is used for accurate eye segmentation. A hierarchial noise removal procedure is applied on the segmented image to get proper eye shape. Then a set of shape features are calculated and trained using nonlinear SVM to get the status of the eye. Experiment shows that the proposed methodology gives excellent segmentation results for both open eyes (both bright and dark pupil) and closed eyes and also classifies correctly.