Model and Dictionary guided Face Inpainting in the Wild
Résumé
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 modied 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.
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