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Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks

文献类型：期刊论文

作者Sun, Yunlian3; Tang, Jinhui3; Sun, Zhenan1,4,5; Tistarelli, Massimo2
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2020
卷号15页码:2960-2972
关键词Face image aging facial expression synthesis generative adversarial networks ordinal ranking
ISSN号1556-6013
DOI10.1109/TIFS.2020.2980792
通讯作者Tang, Jinhui(jinhuitang@njust.edu.cn)
英文摘要Facial image synthesis has been extensively studied, for a long time, in both computer graphics and computer vision. Particularly, the synthesis of face images with varying ages, expressions and poses has received an increasing attention owing to several real-world applications. In this paper, facial age and expression synthesis are addressed. While previous and current research papers on facial age synthesis mostly adopt an age span of 10 years, this paper investigates face aging with a shorter time span. For expression synthesis, given a neutral face, we work on synthesizing faces with varying expression intensities (e.g., from zero to high). Note that both human ages and expression intensities are inherently ordinal. To fully exploit this ordinal nature, we devise ordinal ranking generative adversarial networks (ranking GAN). For each face, a one-hot label is assigned to define its age range/expression intensity. By exploiting the relative order information among age ranges/expression intensities, a binary ranking vector is further computed for each face. In ranking GAN, one-hot labels are used as the condition of the generator for synthesizing faces with target age groups/expression intensities. Moreover, we add a sequence of cost-sensitive ordinal rankers on top of several multi-scale discriminators, with the aim of minimizing age/intensity rank estimation loss when optimizing both the generator and discriminators. In order to evaluate the proposed ranking GAN, extensive experiments are carried out on several public face databases. As demonstrated by the experimental testing, this ranking scheme performs well even when the amount of available labeled training data is limited. The reported experimental results well demonstrate the effectiveness of ranking GAN on synthesizing face aging sequences and faces with varying expression intensities.
WOS关键词MANIPULATION ; APPEARANCE ; PERCEPTION ; FACES ; MODEL ; SHAPE
资助项目National Key Research and Development Program of China[2016YFB1001001] ; National Natural Science Foundation of China[61603391] ; National Natural Science Foundation of China[61925204] ; National Natural Science Foundation of China[61427811] ; Italian Ministry of Research
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000524505300007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Italian Ministry of Research
源URL[http://ir.ia.ac.cn/handle/173211/38916]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Tang, Jinhui
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
2.Univ Sassari, Dept Sci & Informat Technol, I-07100 Sassari, Italy
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
4.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Yunlian,Tang, Jinhui,Sun, Zhenan,et al. Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2020,15:2960-2972.
APA Sun, Yunlian,Tang, Jinhui,Sun, Zhenan,&Tistarelli, Massimo.(2020).Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,15,2960-2972.
MLA Sun, Yunlian,et al."Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 15(2020):2960-2972.

入库方式： OAI收割

来源：自动化研究所

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