Identifying Vandalized Regions in Facial Images of Statues for Inpainting

Historical monuments are considered as one of the key aspects for modern communities. Unfortunately, due to a variety of factors the monuments get damaged. One may think of digitally undoing the damage to the monuments by inpainting, a process to fill-in missing regions in an image. A majority of inpainting techniques reported in the literature require manual selection of the regions to be inpainted. In this paper, we propose a novel method that automates the process of identifying the damage to visually dominant regions viz. eyes, nose and lips in face image of statues, for the purpose of inpainting. First, a bilateral symmetry based method is used to identify the eyes, nose and lips. Textons features are then extracted from each of these regions in a multi-resolution framework to characterize both the regular and irregular textures. These textons are matched with those extracted from a training set of true vandalized and non-vandalized regions, in order to classify the region under consideration. If the region is found to be vandalized, the best matching non-vandalized region from the training set is used to inpaint the identified region using the Poisson image editing method. Experiments conducted on face images of statues downloaded from the Internet, give promising results.