CASIA OpenIR
Global and Local Consistent Wavelet-Domain Age Synthesis
Li, Peipei1,2; Hu, Yibo1; He, Ran1,2; Sun, Zhenan1,2
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN1556-6013
2019-11-01
Volume14Issue:11Pages:2943-2957
Corresponding AuthorHe, Ran(rhe@nlpr.ia.ac.cn)
AbstractAge synthesis is a challenging task due to the complicated and non-linear transformation in the human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN-based methods for age synthesis. To address this issue, we propose a wavelet-domain global and local consistent age generative adversarial network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture details of human faces. Different from the mast existing methods that modeling age synthesis in image domain, we adopt wavelet transform to depict the textual information in frequency domain. Moreover, five types of losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2) identity preserving loss aims to better preserve identity information; 3) age preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise loss aims to preserve the background information of the input face; and 5) the total variation regularization aims to remove ghosting artifacts. Our method is evaluated on three face aging datasets, including CACD2000, Morph, and FG-NET. Qualitative and quantitative experiments show the superiority of the proposed method over other state-of-the-arts.
KeywordAge synthesis wavelet transform generative adversarial network global and local features
DOI10.1109/TIFS.2019.2907973
WOS KeywordPERCEPTION
Indexed BySCI
Language英语
Funding ProjectState Key Development Program[2016YFB1001001] ; State Key Development Program[2017YFC0821602] ; State Key Development Program[2016YFB1001000] ; National Natural Science Foundation of China[61622310] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; Beijing Natural Science Foundation[JQ18017]
Funding OrganizationState Key Development Program ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000474549100001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26883
Collection中国科学院自动化研究所
Corresponding AuthorHe, Ran
Affiliation1.Inst Automat Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat,Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Peipei,Hu, Yibo,He, Ran,et al. Global and Local Consistent Wavelet-Domain Age Synthesis[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2019,14(11):2943-2957.
APA Li, Peipei,Hu, Yibo,He, Ran,&Sun, Zhenan.(2019).Global and Local Consistent Wavelet-Domain Age Synthesis.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,14(11),2943-2957.
MLA Li, Peipei,et al."Global and Local Consistent Wavelet-Domain Age Synthesis".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 14.11(2019):2943-2957.
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