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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2013.134
|2 doi
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|a pubmed24n0779.xml
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Lu, Jiwen
|e verfasserin
|4 aut
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| 245 |
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|a Neighborhood repulsed metric learning for kinship verification
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|c 2014
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|a Text
|b txt
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|a ƒaComputermedien
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|a Date Completed 05.08.2014
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|a Date Revised 05.02.2018
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|a published: Print
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|a CommentIn: IEEE Trans Pattern Anal Mach Intell. 2016 Nov;38(11):2342-2344. - PMID 26829776
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|a Citation Status MEDLINE
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| 520 |
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|a Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers
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|a Comparative Study
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhou, Xiuzhuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tan, Yap-Pen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shang, Yuanyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jie
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 36(2014), 2 vom: 20. Feb., Seite 331-45
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:36
|g year:2014
|g number:2
|g day:20
|g month:02
|g pages:331-45
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2013.134
|3 Volltext
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