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231225s2019 xx |||||o 00| ||eng c |
| 024 |
7 |
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|a 10.1109/TIP.2018.2875346
|2 doi
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| 028 |
5 |
2 |
|a pubmed24n0964.xml
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|a (DE-627)NLM289428521
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Liang, Jianqing
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Weighted Graph Embedding-Based Metric Learning for Kinship Verification
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| 264 |
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1 |
|c 2019
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 08.11.2018
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| 500 |
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|a Date Revised 08.11.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Given a group photograph, it is interesting and useful to judge whether the characters in it share specific kinship relation, such as father-daughter, father-son, mother-daughter, or mother-son. Recently, facial image-based kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Hu, Qinghua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dang, Chuangyin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zuo, Wangmeng
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 28(2019), 3 vom: 10. März, Seite 1149-1162
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:28
|g year:2019
|g number:3
|g day:10
|g month:03
|g pages:1149-1162
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2018.2875346
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