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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2017.2779808
|2 doi
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|a pubmed24n0954.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Tan, Zichang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Efficient Group-n Encoding and Decoding for Facial Age Estimation
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 03.10.2019
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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
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|a Comparative Study
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|a Evaluation Study
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Wan, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lei, Zhen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhi, Ruicong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Guodong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Stan Z
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 11 vom: 14. Nov., Seite 2610-2623
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:40
|g year:2018
|g number:11
|g day:14
|g month:11
|g pages:2610-2623
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2017.2779808
|3 Volltext
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