Semantic feature extraction for accurate eye corner detection

In this paper a novel eye corner (canthus) detection method is proposed. The method is based on sematic features which are extracted from the structure and appearance characteristics of canthus. The eyelids are first fitted to construct an angle model. Based on this model, one feature is proposed to characterize the appearance difference between inner and outer canthus regions, and another feature is proposed to emphasize the role of the canthus angle bisector region. The two features are fused in a logistic regression classifier to detect accurate canthus. The effectiveness and accuracy of the proposed method are demonstrated in experiments.