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CLC number: TP391.41

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Received: 2008-02-26

Revision Accepted: 2008-06-22

Crosschecked: 2008-12-26

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.2 P.232~238

http://doi.org/10.1631/jzus.A0820138


Patch-guided facial image inpainting by shape propagation


Author(s):  Yue-ting ZHUANG, Yu-shun WANG, Timothy K. SHIH, Nick C. TANG

Affiliation(s):  Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yzhuang@cs.zju.edu.cn, eawang@microsoft.com, tshih@cs.tku.edu.tw, nick.ctang@msa.hinet.net

Key Words:  Image inpainting, Face reconstruction, Feature point extraction


Yue-ting ZHUANG, Yu-shun WANG, Timothy K. SHIH, Nick C. TANG. Patch-guided facial image inpainting by shape propagation[J]. Journal of Zhejiang University Science A, 2009, 10(2): 232~238.

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Abstract: 
Images with human faces comprise an essential part in the imaging realm. Occlusion or damage in facial portions will bring a remarkable discomfort and information loss. We propose an algorithm that can repair occluded or damaged facial images automatically, named ‘facial image inpainting’. Inpainting is a set of image processing methods to recover missing image portions. We extend the image inpainting methods by introducing facial domain knowledge. With the support of a face database, our approach propagates structural information, i.e., feature points and edge maps, from similar faces to the missing facial regions. Using the inferred structural information as guidance, an exemplar-based image inpainting algorithm is employed to copy patches in the same face from the source portion to the missing portion. This newly proposed concept of facial image inpainting outperforms the traditional inpainting methods by propagating the facial shapes from a face database, and avoids the problem of variations in imaging conditions from different images by inferring colors and textures from the same face image. Our system produces seamless faces that are hardly seen drawbacks.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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