IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis
Yan, Lan1,2; Zheng, Wenbo1,3; Gou, Chao4; Wang, Fei-Yue1
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2021-11-01
Volume119Pages:13
Abstract

Face photo-sketch synthesis aims to generate face sketches from real photos and vice versa. It can be abstracted as a constrained quantization problem. Although many effort s have been dedicated to this problem, it is still a challenging task to synthesize detail-preserving photos or sketches due to the significant differences between face sketch (drawn by people) and photo (taken by cameras) domains. In this paper, we propose a novel Identity-sensitive Generative Adversarial Network (IsGAN) to address it. Our key insight is to formalize face photo-sketch synthesis as a special case of image-to-image translation and propose to embed identity information through adversarial learning. In particular, an adversarial architecture is used to capture the differences between the two domains, and a new network loss, namely, identity recognition loss is introduced to preserve the detailed identifiable information, which is crucial for photo-sketch synthesis. In addition, to enforce structural consistency during generation, a cyclic-synthesized loss is applied between the generated image of one domain and cycled image of another. The experiments on the CUFS and CUFSF datasets suggest that our model achieves state-of-the-art performance in both qualitative and quantitative measures.

KeywordFace photo-sketch synthesis Image-to-image translation Generative adversarial networks Convolutional neural network Face recognition
DOI10.1016/j.patcog.2021.108077
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018AAA0101502] ; Key Research and Development Program of Guangzhou[2020 07050 0 02] ; Shenzhen Science and Technology Program[RCBS20200714114920272] ; Natural Science Foundation of China[61806198] ; Natural Science Foundation of China[U1811463]
Funding OrganizationNational Key R&D Program of China ; Key Research and Development Program of Guangzhou ; Shenzhen Science and Technology Program ; Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000687401900005
PublisherELSEVIER SCI LTD
Sub direction classification生物特征识别
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45918
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorGou, Chao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China
4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan, Lan,Zheng, Wenbo,Gou, Chao,et al. IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis[J]. PATTERN RECOGNITION,2021,119:13.
APA Yan, Lan,Zheng, Wenbo,Gou, Chao,&Wang, Fei-Yue.(2021).IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis.PATTERN RECOGNITION,119,13.
MLA Yan, Lan,et al."IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis".PATTERN RECOGNITION 119(2021):13.
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