Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Efficient Group-n Encoding and Decoding for Facial Age Estimation | |
Zichang Tan1,2; Jun Wan1; Zhen Lei1; Ruicong Zhi3; Guodong Guo4; Stan Z. Li1 | |
发表期刊 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
ISSN | 0162-8828 |
2018-11-01 | |
卷号 | 40期号:11页码:2610-2623 |
通讯作者 | Wan, Jun(jun.wan@nlpr.ia.ac.cn) |
摘要 | Different 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. |
关键词 | Age estimation deep learning convolutional neural network age grouping data imbalance |
DOI | 10.1109/TPAMI.2017.2779808 |
关键词[WOS] | LEAST-SQUARES REGRESSION ; RECOGNITION ; MANIFOLD ; MODELS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | AuthenMetric RD Funds ; NVIDIA GPU donation program ; Science and Technology Development Fund of Macau[152/2017/A] ; Science and Technology Development Fund of Macau[151/2017/A] ; Science and Technology Development Fund of Macau[112/2014/A3] ; Chinese National Natural Science Foundation Projects[61673052] ; Chinese National Natural Science Foundation Projects[61572536] ; Chinese National Natural Science Foundation Projects[61572501] ; Chinese National Natural Science Foundation Projects[61473291] ; Chinese National Natural Science Foundation Projects[61502491] ; National Key Research and Development Plan[2016YFC0801002] ; National 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 |
项目资助者 | National 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研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000446683700007 |
出版者 | IEEE COMPUTER SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22024 |
专题 | 模式识别国家重点实验室_生物识别与安全技术 |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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