Age estimation via attribute-region association

doi:10.1016/j.neucom.2019.08.034

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Age estimation via attribute-region association
	Chen, Yiliang 1; He, Shengfeng 1; Tan, Zichang2 ; Han, Chu 3; Han, Guoqiang 1; Qin, Jing 4
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2019-11-20
卷号	367 页码:346-356
通讯作者	He, Shengfeng(hesfe@scut.edu.cn)
摘要	Human 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.
关键词	Age estimation Multi-task learning Attribute-region association
DOI	10.1016/j.neucom.2019.08.034
关键词[WOS]	FRAMEWORK ; GENDER ; IMAGE
收录类别	SCI
语种	英语
资助项目	National 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 ; National 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
项目资助者	National 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研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000489017500033
出版者	ELSEVIER
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/26430
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	He, Shengfeng
作者单位	1.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
推荐引用方式 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.