Active Labeling of Facial Feature Points

Although considerable progress has been made in the field of facial feature point detection and tracking, accurate feature point tracking is still very challenging. Manually feature point labeling and correction are time consuming and labor intensive. To alleviate this problem, an active feature point labeling method is proposed in this paper. First, the spatial relations among feature points are modeled by a Bayesian Network. Second, the mutual information between a feature point and the remaining feature points is calculated in two steps: in the first step, to identify the most informative facial region, the mutual information between one facial sub-region and the other sub-regions is calculated; in the second step, the mutual information between one feature point and the other feature points in the most informative facial sub-region is established to rank the facial feature points. Users provide labels of the feature points according to their mutual information in descending order. After that, the human corrections and the image measurements are integrated by the Bayesian Network to produce the refined annotations. Simulative experiments on the extended Cohn-Kanade (CK+) database demonstrate the effectiveness of our approach.