PUBLICATION DATE

2015-05-10

AUTHORS

Yi Jin, Yizhi Wang, Jiuwen Cao, Ruicong Zhi

TITLE

Ensemble based extreme learning machine for cross-modality face matching

ISSUE

19

VOLUME

75

ISSN (print)

1380-7501

ISSN (electronic)

1573-7721

ABSTRACT

Extreme learning machine (ELM) is one of the most important and efficient machine learning algorithms for pattern classification due to its fast learning speed. In this paper, we propose a new ensemble based ELM approach for cross-modality face matching. Different to traditional face recognition methods, the proposed approach integrates the voting-base extreme learning machine (V-ELM) with a novel feature learning based face descriptor. Firstly, the discriminant feature learning is proposed to learn the cross-modality feature representation. Then, we used common subspace learning based method to reduce the obtained cross-modality features. Finally, Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process. Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach.

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38 TRIPLES      30 PREDICATES      39 URIs      22 LITERALS

Subject Predicate Object
1 articles:4625d22aaf36a8def3361c6a1d74cc3b sg:abstract Abstract Extreme learning machine (ELM) is one of the most important and efficient machine learning algorithms for pattern classification due to its fast learning speed. In this paper, we propose a new ensemble based ELM approach for cross-modality face matching. Different to traditional face recognition methods, the proposed approach integrates the voting-base extreme learning machine (V-ELM) with a novel feature learning based face descriptor. Firstly, the discriminant feature learning is proposed to learn the cross-modality feature representation. Then, we used common subspace learning based method to reduce the obtained cross-modality features. Finally, Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process. Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach.
2 sg:articleType OriginalPaper
3 sg:coverYear 2016
4 sg:coverYearMonth 2016-10
5 sg:ddsId s11042-015-2650-1
6 sg:ddsIdJournalBrand 11042
7 sg:doi 10.1007/s11042-015-2650-1
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22 Web of Science
23 sg:issnElectronic 1573-7721
24 sg:issnPrint 1380-7501
25 sg:issue 19
26 sg:language English
27 sg:license http://scigraph.springernature.com/explorer/license/
28 sg:pageEnd 11846
29 sg:pageStart 11831
30 sg:publicationDate 2015-05-10
31 sg:publicationYear 2015
32 sg:publicationYearMonth 2015-05
33 sg:scigraphId 4625d22aaf36a8def3361c6a1d74cc3b
34 sg:title Ensemble based extreme learning machine for cross-modality face matching
35 sg:volume 75
36 sg:webpage https://link.springer.com/10.1007/s11042-015-2650-1
37 rdf:type sg:Article
38 rdfs:label Article: Ensemble based extreme learning machine for cross-modality face matching
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