MHGAN: Multi-Hierarchies Generative Adversarial Network for High-Quality Face Sketch Synthesis

IEEE Access

Kangning Du¹  Lin Cao¹  Tao Wang¹  Huaqiang Zhou¹  Yanan Guo¹ 

[1] Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China;

关键词: Face sketch synthesis;    generative adversarial network;    facial feature extractor;    multi-hierarchies GAN;   

DOI  :  10.1109/ACCESS.2020.3041284

来源: DOAJ

【 摘 要 】

Face sketch synthesis has made significant progress in the past few years. Recently, GAN-based methods have shown promising results on image-to-image translation problems, especially photo-to-sketch synthesis. Because the facial sketch has a hyper-abstract style and continuous graphic elements, compared with other image styles, its local details are easier to expose small artifacts and blur. The existing face sketch synthesis methods lack models for specific facial regions and usually generate face sketches with coarse structures. To synthesis high-quality sketches and overcome the blurs and deformations, this paper proposes a novel Multi-Hierarchies GAN, which divides the face image into multiple hierarchical structures to learn different regions' features of the face. It includes three modules: a local region module, mask module, and fusion module. The local region module can learn the detailed features of different local regions of the face by GAN. The mask module can generate a coarse facial structure of a sketch and uses the facial feature extractor to enhance the high-level image and learn the latent spaces' feature. The fusion module can generate the final sketch by combining fine local regions and coarse facial structure. Extensive qualitative and quantitative experiments illustrate that the proposed method outperforms the state-of-the-art methods on the CUFS and CUFSF standard datasets and photos on the internet.

【 授权许可】

Unknown