Semi-Supervised Learning for Face Sketch Synthesis in the Wild

12 Dec 2018  ·  Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong ·

Face sketch synthesis has made great progress in the past few years. Recent methods based on deep neural networks are able to generate high quality sketches from face photos... However, due to the lack of training data (photo-sketch pairs), none of such deep learning based methods can be applied successfully to face photos in the wild. In this paper, we propose a semi-supervised deep learning architecture which extends face sketch synthesis to handle face photos in the wild by exploiting additional face photos in training. Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs. We then compose a pseudo sketch feature representation using the corresponding sketch feature patches to supervise our network. With the proposed approach, we can train our networks using a small reference set of photo-sketch pairs together with a large face photo dataset without ground truth sketches. Experiments show that our method achieve state-of-the-art performance both on public benchmarks and face photos in the wild. Codes are available at https://github.com/chaofengc/Face-Sketch-Wild. read more

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Face Sketch Synthesis CUFS Residual net + Pseudo Sketch Feature Loss + LSGAN FSIM 72.56% # 1
SSIM 54.63% # 1
Face Sketch Synthesis CUFSF Residual net + Pseudo Sketch Feature Loss + LSGAN FSIM 71.59% # 3
SSIM 40.85% # 1
Face Sketch Synthesis CUHK Residual net + Pseudo Sketch Feature Loss + LSGAN SSIM 63.28% # 1
FSIM 74.23% # 1

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