Using targeted statistics for face regeneration

Face occlusion is a common problem that occurs in applications that analyze images for faces, e.g. detection, tracking and recognition. The presence of occlusion can adversely affect such face processing algorithms. This paper proposes a solution to the problem: we attempt to remove the occlusion by considering it as a damaged part that needs to be regenerated. More precisely, our technique learns the statistical correlation between different regions of the face without enforcing left-right symmetry. However, we learn only from face images that are similar to the target face (i.e. the face dataset is filtered to retain only similar faces). We show that such targeted statistics yield better results than statistics learned from faces in general. The occluded region is then regenerated by predicting its appearance from the most correlated unoccluded region of the same face. We also study how different factors influence our face regeneration technique: the effect of filtering the dataset; the presence/ absence of the target face during learning; the location of the occluded region; and the size of the occlusion. Our work can be used as a pre-processing step for face processing algorithms, or simply to enhance a face image for human viewing.