Face sketch synthesis: a survey

2021 ◽  
Author(s):  
Hongbo Bi ◽  
Ziqi Liu ◽  
Lina Yang ◽  
Kang Wang ◽  
Ning Li
Keyword(s):  
Sketch Synthesis ◽  
Face Sketch Synthesis

scholarly journals Generative adversarial learning for detail-preserving face sketch synthesis

2021 ◽  
Vol 438 ◽  
pp. 107-121
Author(s):  
Weiguo Wan ◽  
Yong Yang ◽  
Hyo Jong Lee
Keyword(s):  
Adversarial Learning ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

Face sketch synthesis using non-local means and patch-based seaming

2015 ◽  
Author(s):  
Liang Chang ◽  
Yves Rozenholc ◽  
Xiaoming Deng ◽  
Fuqing Duan ◽  
Mingquan Zhou
Keyword(s):  
Local Means ◽  
Non Local ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

Anchored Neighborhood Index for Face Sketch Synthesis

2018 ◽  
Vol 28 (9) ◽  
pp. 2154-2163 ◽  
Author(s):  
Nannan Wang ◽  
Xinbo Gao ◽  
Leiyu Sun ◽  
Jie Li
Keyword(s):  
Sketch Synthesis ◽  
Face Sketch Synthesis

scholarly journals Markov Random Neural Fields for Face Sketch Synthesis

2018 ◽  
Author(s):  
Mingjin Zhang ◽  
Nannan Wang ◽  
Xinbo Gao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Specific Information ◽  
Neural Fields ◽  
Common Information ◽  
Common Structure ◽  
Markov Random ◽  
The Common ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

Synthesizing face sketches with both common and specific information from photos has been recently attracting considerable attentions in digital entertainment. However, the existing approaches either make the strict similarity assumption on face sketches and photos, leading to lose some identity-specific information, or learn the direct mapping relationship from face photos to sketches by the simple neural network, resulting in the lack of some common information. In this paper, we propose a novel face sketch synthesis based on the Markov random neural fields including two structures. In the first structure, we utilize the neural network to learn the non-linear photo-sketch relationship and obtain the identity-specific information of the test photo, such as glasses, hairpins and hairstyles. In the second structure, we choose the nearest neighbors of the test photo patch and the sketch pixel synthesized in the first structure from the training data which ensure the common information of Miss or Mr Average. Experimental results on the Chinese University of Hong Kong face sketch database illustrate that our proposed framework can preserve the common structure and capture the characteristic features. Compared with the state-of-the-art methods, our method achieves better results in terms of both quantitative and qualitative experimental evaluations.

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