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Age Estimation via Selecting Discriminated Features and Preserving Geometry

文献类型：期刊论文

作者Tian, Qing1,2,3; Sun, Heyang3; Ma, Chuang3; Cao, Meng3; Chu, Yi3
刊名KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
出版日期2020-04-30
卷号14期号:4页码:1721-1737
关键词Age Estimation Least Square Regression Cumulative Attribute Coding Feature Selection Manifold Learning
ISSN号1976-7277
DOI10.3837/tiis.2020.04.017
通讯作者Tian, Qing(tianqing@nuist.edu.cn)
英文摘要Human apparent age estimation has become a popular research topic and attracted great attention in recent years due to its wide applications, such as personal security and law enforcement. To achieve the goal of age estimation, a large number of methods have been pro-posed, where the models derived through the cumulative attribute coding achieve promised performance by preserving the neighbor-similarity of ages. However, these methods afore-mentioned ignore the geometric structure of extracted facial features. Indeed, the geometric structure of data greatly affects the accuracy of prediction. To this end, we propose an age estimation algorithm through joint feature selection and manifold learning paradigms, so-called Feature-selected and Geometry-preserved Least Square Regression (FGLSR). Based on this, our proposed method, compared with the others, not only preserves the geometry structures within facial representations, but also selects the discriminative features. Moreover, a deep learning extension based FGLSR is proposed later, namely Feature selected and Geometry preserved Neural Network (FGNN). Finally, related experiments are conducted on Morph2 and FG-Net datasets for FGLSR and on Morph2 datasets for FGNN. Experimental results testify our method achieve the best performances.
资助项目National Natural Science Foundation of China[61702273] ; Natural Science Foundation of Jiangsu Province[BK20170956] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[17KJB520022] ; Fundamental Research Funds for the Central Universities[NJ2019010] ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund ; Open Projects Program of National Laboratory of Pattern Recognition[202000007]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000530882000015
出版者KSII-KOR SOC INTERNET INFORMATION
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; Fundamental Research Funds for the Central Universities ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund ; Open Projects Program of National Laboratory of Pattern Recognition
源URL[http://ir.ia.ac.cn/handle/173211/39478]  
专题自动化研究所_个人空间
通讯作者Tian, Qing
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Atmospher Environm & Equip, Nanjing 210044, Peoples R China
3.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Qing,Sun, Heyang,Ma, Chuang,et al. Age Estimation via Selecting Discriminated Features and Preserving Geometry[J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS,2020,14(4):1721-1737.
APA Tian, Qing,Sun, Heyang,Ma, Chuang,Cao, Meng,&Chu, Yi.(2020).Age Estimation via Selecting Discriminated Features and Preserving Geometry.KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS,14(4),1721-1737.
MLA Tian, Qing,et al."Age Estimation via Selecting Discriminated Features and Preserving Geometry".KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 14.4(2020):1721-1737.

入库方式： OAI收割

来源：自动化研究所

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