CASIA OpenIR
Age estimation via attribute-region association
Chen, Yiliang1; He, Shengfeng1; Tan, Zichang2; Han, Chu3; Han, Guoqiang1; Qin, Jing4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-11-20
Volume367Pages:346-356
Corresponding AuthorHe, Shengfeng(hesfe@scut.edu.cn)
AbstractHuman age has been treated as an important biometric trait in many practical applications. In this paper, we propose an Attribute-Region Association Network (ARAN) to tackle the challenging age estimation problem. Instead of performing prediction from a global perspective, we delve into the relationship between face attributes and regions. First, the proposed network is guided by the auxiliary demographic information, as different demographic information (e.g., gender and ethnicity) intrinsically correlates to human age. Second, different face components are separately handled and then involved in the proposed ensemble network, as these components vary differently along with human age. To explore both global and local information, the proposed network consists of several sub-network, each of them takes the global face and a face sub-region as input. Each sub-network leverages the intrinsic correlation between different face attributes (i.e., age, gender, and ethnicity), and it is trained in a multi-task manner. These attribute-region sub-networks are associated to yield the final predictions. To properly train and coordinate such a complex network, a new hierarchical-scheduling training method is proposed to balance the learning complexity in the multi-task learning. In this way, the performance of the most difficult task (i.e., age estimation) can be significantly improved. Extensive experiments on the MORPH Album II and FG-NET show that the proposed method outperforms the state-of-the-art age estimation methods by a significant margin. In particular, for the challenging age estimation, the Mean Absolute Errors (MAE) are decreased to 2.51 years compared to the state-of-the-arts on the MORPH Album II dataset. (C) 2019 Elsevier B.V. All rights reserved.
KeywordAge estimation Multi-task learning Attribute-region association
DOI10.1016/j.neucom.2019.08.034
WOS KeywordFRAMEWORK ; GENDER ; IMAGE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61472145] ; National Natural Science Foundation of China[61972162] ; National Natural Science Foundation of China[61702194] ; Innovation and Technology Fund of Hong Kong[ITS/319/17] ; Special Fund of Science and Technology Research and Development on Application From Guangdong Province (SFSTRDA-GD)[2016B010127003] ; Guangzhou Key Industrial Technology Research fund[201802010036] ; Guangdong Natural Science Foundation[2017A030312008] ; CCFTencent Openfund
Funding OrganizationNational Natural Science Foundation of China ; Innovation and Technology Fund of Hong Kong ; Special Fund of Science and Technology Research and Development on Application From Guangdong Province (SFSTRDA-GD) ; Guangzhou Key Industrial Technology Research fund ; Guangdong Natural Science Foundation ; CCFTencent Openfund
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000489017500033
PublisherELSEVIER
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26430
Collection中国科学院自动化研究所
Corresponding AuthorHe, Shengfeng
Affiliation1.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
4.Hong Kong Polytech Univ, Dept Nursing, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Chen, Yiliang,He, Shengfeng,Tan, Zichang,et al. Age estimation via attribute-region association[J]. NEUROCOMPUTING,2019,367:346-356.
APA Chen, Yiliang,He, Shengfeng,Tan, Zichang,Han, Chu,Han, Guoqiang,&Qin, Jing.(2019).Age estimation via attribute-region association.NEUROCOMPUTING,367,346-356.
MLA Chen, Yiliang,et al."Age estimation via attribute-region association".NEUROCOMPUTING 367(2019):346-356.
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