Abstract
Face sketch synthesis has many practical applications, such as law enforcement and digital entertainment. Existing face sketch synthesis methods focus on neighbor selection and/or weight reconstruction. However, these approaches did not take “interpretation through synthesis” into consideration obviously. Active appearance model (AAM) is one of “interpretation through synthesis” approaches. In this paper, we introduce AAM to “explain” face photos by generating synthetic images that are as similar as possible. Then AAM provides a compact set of parameters that are useful for face sketch synthesis. Extensive experiments on public face sketch databases demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.
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Acknowledgements
This work is supported by the National Key R&D Program (No.2017YFC011300, and No.2016YFB1001503), the Nature Science Foundation of China (No.61422210, No.61772443, No.61373076, No.61402388 and No.61572410), the Post Doctoral Innovative Talent Support Program under Grant BX201600094, the China Post-Doctoral Science Foundation under Grant 2017M612134 and the Nature Science Foundation of Fujian Province, China (No. 2017J01125).
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Zhang, S., Ji, R. AAM Based Face Sketch Synthesis. Neural Process Lett 48, 1405–1414 (2018). https://doi.org/10.1007/s11063-018-9782-z
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DOI: https://doi.org/10.1007/s11063-018-9782-z