Face Sketch Synthesis Based on Local and Nonlocal Similarity Regularization


Songze Tang, Xuhuan Zhou, Nan Zhou, Le Sun, Jin Wang, Journal of Information Processing Systems Vol. 15, No. 6, pp. 1449-1461, Dec. 2019  

10.3745/JIPS.02.0125
Keywords: Face Sketch Synthesis, local similarity, nonlocal similarity, Patch Representation
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Abstract

Face sketch synthesis plays an important role in public security and digital entertainment. In this paper, we present a novel face sketch synthesis method via local similarity and nonlocal similarity regularization terms. The local similarity can overcome the technological bottlenecks of the patch representation scheme in traditional learning-based methods. It improves the quality of synthesized sketches by penalizing the dissimilar training patches (thus have very small weights or are discarded). In addition, taking the redundancy of image patches into account, a global nonlocal similarity regularization is employed to restrain the generation of the noise and maintain primitive facial features during the synthesized process. More robust synthesized results can be obtained. Extensive experiments on the public databases validate the generality, effectiveness, and robustness of the proposed algorithm.


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Cite this article
[APA Style]
Songze Tang, Xuhuan Zhou, Nan Zhou, Le Sun, & Jin Wang (2019). Face Sketch Synthesis Based on Local and Nonlocal Similarity Regularization. Journal of Information Processing Systems, 15(6), 1449-1461. DOI: 10.3745/JIPS.02.0125.

[IEEE Style]
S. Tang, X. Zhou, N. Zhou, L. Sun and J. Wang, "Face Sketch Synthesis Based on Local and Nonlocal Similarity Regularization," Journal of Information Processing Systems, vol. 15, no. 6, pp. 1449-1461, 2019. DOI: 10.3745/JIPS.02.0125.

[ACM Style]
Songze Tang, Xuhuan Zhou, Nan Zhou, Le Sun, and Jin Wang. 2019. Face Sketch Synthesis Based on Local and Nonlocal Similarity Regularization. Journal of Information Processing Systems, 15, 6, (2019), 1449-1461. DOI: 10.3745/JIPS.02.0125.