Face Alignment Robust to Occlusion

In this paper we present a new approach to robustly align facial features to a face image even when the face is partially occluded. Previous methods are vulnerable to partial occlusion of the face, since it is assumed, explicitly or implicitly, that there is no significant occlusion. In order to cope with this difficulty, our approach relies on two schemes: one is explicit multi-modal representation of the response from each of the face feature detectors, and the other is RANSAC hypothesizeand-test search for the correct alignment over subset samplings of those response modes. We evaluated the proposed method on a large number of facial images, occluded and non-occluded. The results demonstrated that the alignment is accurate and stable over a wide range of degrees and variations of occlusion.