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Face Sketch Synthesis Based on Adaptive Similarity Regularization

  • Songze TangEmail author
  • Mingyue Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)

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 patch-based face sketch synthesis 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 are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm.

Keywords

Face sketch synthesis Local similarity Nonlocal similarity 

Notes

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China under Grant 61702269, and Grant 61671339, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20171074. The Fundamental Research Funds for the Central Universities at Nanjing Forest Police College under Grant No. LGZD201702.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Criminal Science and TechnologyNanjing Forest Police CollegeNanjingChina
  2. 2.Department of Information and TechnologyNanjing Forest Police CollegeNanjingChina

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