CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Human Age Estimation Based on Locality and Ordinal Information
Li, Changsheng1; Liu, Qingshan2; Dong, Weishan1; Zhu, Xiaobin3; Liu, Jing4; Lu, Hanqing4
AbstractIn this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.
KeywordAge Estimation Feature Selection Local Manifold Structure Ordinal Pattern Semi-supervised Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61272223 ; Natural Science Foundation of Jiangsu Province, China(BK2012045) ; 61402023)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000363233000013
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Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.IBM Res China, Beijing 100094, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Sch Informat & Control, B DAT Lab, Nanjing 210044, Jiangsu, Peoples R China
3.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Changsheng,Liu, Qingshan,Dong, Weishan,et al. Human Age Estimation Based on Locality and Ordinal Information[J]. IEEE TRANSACTIONS ON CYBERNETICS,2015,45(11):2522-2534.
APA Li, Changsheng,Liu, Qingshan,Dong, Weishan,Zhu, Xiaobin,Liu, Jing,&Lu, Hanqing.(2015).Human Age Estimation Based on Locality and Ordinal Information.IEEE TRANSACTIONS ON CYBERNETICS,45(11),2522-2534.
MLA Li, Changsheng,et al."Human Age Estimation Based on Locality and Ordinal Information".IEEE TRANSACTIONS ON CYBERNETICS 45.11(2015):2522-2534.
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