Gender classification from unaligned facial images using support subspaces
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| Title: | Gender classification from unaligned facial images using support subspaces |
|---|---|
| Authors: | Chu, Wen-Sheng1, Huang, Chun-Rong2 crhuang@cs.nchu.edu.tw, Chen, Chu-Song1 |
| Source: | Information Sciences. Feb2013, Vol. 221, p98-109. 12p. |
| Subjects: | Biometric identification, Classification, Subspaces (Mathematics), Performance evaluation, Support vector machines, Statistical correlation |
| Abstract: | Abstract: Rough face alignments result in suboptimal performance of face identification. In this study, we present an approach for identifying the gender based on facial images without proper face alignments. Instead of just using only the detected face patch for identification, a set of patches is randomly cropped around the face detection region. Each patch set is represented by a linear subspace and compared with other linear subspaces by measuring their canonical correlations. A similarity matrix comprised of the canonical correlations is then incorporated into an indefinite-kernel Support Vector Machine (SVM) formulation. The number of support vectors, which we call support subspaces, can be decided automatically, hence, we can avoid the dimension selection problem observed in our previous work. Our experimental results demonstrate that the proposed approach outperforms state-of-the-art methods. [Copyright &y& Elsevier] |
| Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 83324136 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Gender classification from unaligned facial images using support subspaces – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chu%2C+Wen-Sheng%22">Chu, Wen-Sheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Chun-Rong%22">Huang, Chun-Rong</searchLink><relatesTo>2</relatesTo><i> crhuang@cs.nchu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Chu-Song%22">Chen, Chu-Song</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Information+Sciences%22">Information Sciences</searchLink>. Feb2013, Vol. 221, p98-109. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Subspaces+%28Mathematics%29%22">Subspaces (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Rough face alignments result in suboptimal performance of face identification. In this study, we present an approach for identifying the gender based on facial images without proper face alignments. Instead of just using only the detected face patch for identification, a set of patches is randomly cropped around the face detection region. Each patch set is represented by a linear subspace and compared with other linear subspaces by measuring their canonical correlations. A similarity matrix comprised of the canonical correlations is then incorporated into an indefinite-kernel Support Vector Machine (SVM) formulation. The number of support vectors, which we call support subspaces, can be decided automatically, hence, we can avoid the dimension selection problem observed in our previous work. Our experimental results demonstrate that the proposed approach outperforms state-of-the-art methods. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ins.2012.09.008 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 98 Subjects: – SubjectFull: Biometric identification Type: general – SubjectFull: Classification Type: general – SubjectFull: Subspaces (Mathematics) Type: general – SubjectFull: Performance evaluation Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Statistical correlation Type: general Titles: – TitleFull: Gender classification from unaligned facial images using support subspaces Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chu, Wen-Sheng – PersonEntity: Name: NameFull: Huang, Chun-Rong – PersonEntity: Name: NameFull: Chen, Chu-Song IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 00200255 Numbering: – Type: volume Value: 221 Titles: – TitleFull: Information Sciences Type: main |
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