This work presents a method that can be used to inpaint occluded facial regions with unconstrained pose and orientation. This approach rst warps the facial region onto a reference model to synthe- size a frontal view. A modied Robust Principal Component Analysis (RPCA) approach is then used to suppress warping errors. It then uses a novel local patch-based face inpainting algorithm which hallucinates missing pixels using a dictionary of face images which are pre-aligned to the same reference model. The hallucinated region is then warped back onto the original image to restore missing pixels. Experimental results on synthetic occlusions demonstrate that the pro- posed face inpainting method has the best performance achieving PSNR gains of up to 0.74dB over the second-best method. Moreover, experi- ments on the COFW dataset and a number of real-world images show that the proposed method successfully restores occluded facial regions in the wild even for CCTV quality images.