A(3)GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging | |
Liu, Yunfan1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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ISSN | 1556-6013 |
2021 | |
卷号 | 16页码:2776-2790 |
摘要 | Face aging has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information due to the insufficient consideration of prior knowledge of input faces, and 2) distortions of low-level image content (e.g. modifications in age-irrelevant regions). In this article, we introduce A(3)GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Unlike previous works with attention modules, we introduce face parsing maps to help the generator distinguish image regions of interest and suppress attention activation elsewhere. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photo-realistic aged face images and achieving state-of-the-art performance on popular datasets. |
关键词 | Aging Faces Face recognition Facial features Generators Wavelet packets Visualization Generative adversarial networks face aging facial attribute attention mechanism wavelet packet transform |
DOI | 10.1109/TIFS.2021.3065499 |
关键词[WOS] | PERCEPTION ; MODEL |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Natural Science Foundation of China[U1836217] ; Natural Science Foundation of China[62076240] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61702513] ; Artificial Intelligence Research, Chinese Academy of Sciences (CAS-AIR) ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project)[2019JZZY010119] |
项目资助者 | Natural Science Foundation of China ; Artificial Intelligence Research, Chinese Academy of Sciences (CAS-AIR) ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000639651900008 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44353 |
专题 | 模式识别实验室 |
通讯作者 | Sun, Zhenan |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Artificial Intelligence Res, Qingdao 266300, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Yunfan,Li, Qi,Sun, Zhenan,et al. A(3)GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2021,16:2776-2790. |
APA | Liu, Yunfan,Li, Qi,Sun, Zhenan,&Tan, Tieniu.(2021).A(3)GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,16,2776-2790. |
MLA | Liu, Yunfan,et al."A(3)GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 16(2021):2776-2790. |
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A3GAN_An_Attribute-A(4213KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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