Model and Dictionary guided Face Inpainting in the Wild

Reuben Farrugia 1 Christine Guillemot 2
2 Sirocco - Analysis representation, compression and communication of visual data
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
Abstract : 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.
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Reuben Farrugia, Christine Guillemot. Model and Dictionary guided Face Inpainting in the Wild. ACCV workshop on New Trends in Image Restoration and Enhancement, Nov 2016, Taipei, Taiwan. pp.17. ⟨hal-01388971⟩

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