Anchored neighborhoods search based on global dictionary atoms for face photo-sketch synthesis

Feng Liu; Ran Xu; Jieying Zheng; Qiuli Lin; Zongliang Gan

doi:10.1117/12.2539810

14 August 2019 Anchored neighborhoods search based on global dictionary atoms for face photo-sketch synthesis

Feng Liu, Ran Xu, Jieying Zheng, Qiuli Lin, Zongliang Gan

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790D (2019) https://doi.org/10.1117/12.2539810
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size normalize. To break through the limitation, we propose a global face sketch synthesis method: In training, all training photo-sketch patch pairs are collected together and a photo feature dictionary is learned from the photo patches. For each atom of the dictionary, its K closest photo-sketch patch pairs are clustered, namely “Anchored Neighborhood”. In testing, for each test photo patch, we search its nearest photo patch in the Anchored Neighborhood determined by its closest atom, then the corresponding sketch patch is the output. By the same way, we train and test in the high-frequency domain and synthesis the high-frequency results. Finally, the fusion of the initial and the high-frequency results is the final sketch. The experiments on three public face sketch datasets and various real-world photos demonstrate the effectiveness and robustness of the proposed method

Citation Download Citation

Feng Liu, Ran Xu, Jieying Zheng, Qiuli Lin, and Zongliang Gan "Anchored neighborhoods search based on global dictionary atoms for face photo-sketch synthesis", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790D (14 August 2019); https://doi.org/10.1117/12.2539810

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