Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA.
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| Title: | Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA. |
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| Authors: | Bhowmik, Mrinal Kanti1 mrinalkantibhowmik@tripurauniv.in, Saha, Priya1, Singha, Anu1, Bhattacharjee, Debotosh2 debotosh@ieee.org, Dutta, Paramartha3 paramartha@ieee.org |
| Source: | Expert Systems with Applications. Feb2019, Vol. 116, p96-107. 12p. |
| Subjects: | Face perception testing, Robust control, Random noise theory, Independent component analysis, Probability density function, Human-computer interaction |
| Abstract: | Highlights • The proposed method works on face recognition as well as facial expression recognition system. • The method recognizes noisy face images also. • Log-ICA II performs better than Log-ICA I. Abstract By reducing the gaussianity, Independent Component Analysis (ICA) behaves robustly in segregating individual signals of non-skewed characteristic from a mixed composite signal. In this article, we present a next-generation variant of ICA, especially applicable in the skewed composite signal scenario, applying the Logarithmic transformation on basic ICA, named as Log-ICA. This approach is capable of decreasing overlapping probability densities of the composite signal, which, in turn, extracts more independent components because of reduced gaussianity. Here also we use two different architectures Log-ICA I and Log-ICA II corresponding to two variants of ICA architecture (ICA I and ICA II). We justify the effectiveness of the proposed technique on five separate benchmark face datasets using five classifiers. Out of five face datasets, two datasets contain both visible and thermal face images. Experimental results show that Log-ICA II performs better than Log-ICA I and two variants of ICA for original face images and noise-induced face images. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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: 132426796 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bhowmik%2C+Mrinal+Kanti%22">Bhowmik, Mrinal Kanti</searchLink><relatesTo>1</relatesTo><i> mrinalkantibhowmik@tripurauniv.in</i><br /><searchLink fieldCode="AR" term="%22Saha%2C+Priya%22">Saha, Priya</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Singha%2C+Anu%22">Singha, Anu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bhattacharjee%2C+Debotosh%22">Bhattacharjee, Debotosh</searchLink><relatesTo>2</relatesTo><i> debotosh@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Dutta%2C+Paramartha%22">Dutta, Paramartha</searchLink><relatesTo>3</relatesTo><i> paramartha@ieee.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Feb2019, Vol. 116, p96-107. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Face+perception+testing%22">Face perception testing</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+component+analysis%22">Independent component analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights • The proposed method works on face recognition as well as facial expression recognition system. • The method recognizes noisy face images also. • Log-ICA II performs better than Log-ICA I. Abstract By reducing the gaussianity, Independent Component Analysis (ICA) behaves robustly in segregating individual signals of non-skewed characteristic from a mixed composite signal. In this article, we present a next-generation variant of ICA, especially applicable in the skewed composite signal scenario, applying the Logarithmic transformation on basic ICA, named as Log-ICA. This approach is capable of decreasing overlapping probability densities of the composite signal, which, in turn, extracts more independent components because of reduced gaussianity. Here also we use two different architectures Log-ICA I and Log-ICA II corresponding to two variants of ICA architecture (ICA I and ICA II). We justify the effectiveness of the proposed technique on five separate benchmark face datasets using five classifiers. Out of five face datasets, two datasets contain both visible and thermal face images. Experimental results show that Log-ICA II performs better than Log-ICA I and two variants of ICA for original face images and noise-induced face images. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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.eswa.2018.08.047 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 96 Subjects: – SubjectFull: Face perception testing Type: general – SubjectFull: Robust control Type: general – SubjectFull: Random noise theory Type: general – SubjectFull: Independent component analysis Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Human-computer interaction Type: general Titles: – TitleFull: Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bhowmik, Mrinal Kanti – PersonEntity: Name: NameFull: Saha, Priya – PersonEntity: Name: NameFull: Singha, Anu – PersonEntity: Name: NameFull: Bhattacharjee, Debotosh – PersonEntity: Name: NameFull: Dutta, Paramartha IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 116 Titles: – TitleFull: Expert Systems with Applications Type: main |
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