Interactive labeling of facial action units

For many computer vision problems, it is very important to produce the ground truth data. Manual data labeling is labor-intensive and prone to the human errors, whereas fully automatic data labeling is not feasible and reliable. In this paper, we propose an interactive labeling technique for efficient and accurate data labeling. Constructed on a Bayesian network (BN), the automatic image labeler produces an initial labeling of the image. A human then examines the initial labeling and makes minor corrections. The human corrections and the image measurements are then integrated by the BN framework to produce a refined labeling. We demonstrate the capability of this technique on labeling facial action units.