CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Efficient Group-n Encoding and Decoding for Facial Age Estimation
Zichang Tan1,2; Jun Wan1; Zhen Lei1; Ruicong Zhi3; Guodong Guo4; Stan Z. Li1
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2018-11-01
Volume40Issue:11Pages:2610-2623
Corresponding AuthorWan, Jun(jun.wan@nlpr.ia.ac.cn)
AbstractDifferent ages are closely related especially among the adjacent ages because aging is a slow and extremely non-stationary process with much randomness. To explore the relationship between the real age and its adjacent ages, an age group-n encoding (AGEn) method is proposed in this paper. In our model, adjacent ages are grouped into the same group and each age corresponds to n groups. The ages grouped into the same group would be regarded as an independent class in the training stage. On this basis, the original age estimation problem can be transformed into a series of binary classification sub-problems. And a deep Convolutional Neural Networks (CNN) with multiple classifiers is designed to cope with such sub-problems. Later, a Local Age Decoding (LAD) strategy is further presented to accelerate the prediction process, which locally decodes the estimated age value from ordinal classifiers. Besides, to alleviate the imbalance data learning problem of each classifier, a penalty factor is inserted into the unified objective function to favor the minority class. To compare with state-of-the-art methods, we evaluate the proposed method on FG-NET. MORPH II, CACD and Chalearn LAP 2015 databases and it achieves the best performance.
KeywordAge estimation deep learning convolutional neural network age grouping data imbalance
DOI10.1109/TPAMI.2017.2779808
WOS KeywordLEAST-SQUARES REGRESSION ; RECOGNITION ; MANIFOLD ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan[2016YFC0801002] ; Chinese National Natural Science Foundation Projects[61502491] ; Chinese National Natural Science Foundation Projects[61473291] ; Chinese National Natural Science Foundation Projects[61572501] ; Chinese National Natural Science Foundation Projects[61572536] ; Chinese National Natural Science Foundation Projects[61673052] ; Science and Technology Development Fund of Macau[112/2014/A3] ; Science and Technology Development Fund of Macau[151/2017/A] ; Science and Technology Development Fund of Macau[152/2017/A] ; NVIDIA GPU donation program ; AuthenMetric RD Funds
Funding OrganizationNational Key Research and Development Plan ; Chinese National Natural Science Foundation Projects ; Science and Technology Development Fund of Macau ; NVIDIA GPU donation program ; AuthenMetric RD Funds
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000446683700007
PublisherIEEE COMPUTER SOC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22024
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.School of Computer and Communication Engineering, University of Science and Technology Beijing
4.Lane Department of Computer Science and Electrical Engineering, West Virginia University
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zichang Tan,Jun Wan,Zhen Lei,et al. Efficient Group-n Encoding and Decoding for Facial Age Estimation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2018,40(11):2610-2623.
APA Zichang Tan,Jun Wan,Zhen Lei,Ruicong Zhi,Guodong Guo,&Stan Z. Li.(2018).Efficient Group-n Encoding and Decoding for Facial Age Estimation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,40(11),2610-2623.
MLA Zichang Tan,et al."Efficient Group-n Encoding and Decoding for Facial Age Estimation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40.11(2018):2610-2623.
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