Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network.
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| Title: | Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network. |
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| Authors: | Rai, Ashok Kumar1 (AUTHOR) akraiceg@gmail.com, Senthilkumar, Radha1 (AUTHOR), R, Aswin Kumar1 (AUTHOR) |
| Source: | Microprocessors & Microsystems. Jul2020, Vol. 76, pN.PAG-N.PAG. 1p. |
| Subjects: | Convolutional neural networks, Human facial recognition software, Pixels, Face |
| Abstract: | A Hyperspectral camera provides discriminating features for capturing human faces that cannot be obtained by any other imaging technique. Nevertheless, it has new issues comprising curse of dimensionality, physical parameter retrieval, fast computing and inter band misalignment. As a result of, the literature of Hyperspectral Face Recognition is more scanty and confined to improvised dimensionality reduction and minimization of wide-ranging bands, and thus the objective can be obtained by the use of the Convolution Neural Network (ConvNet). Since ConvNet is of great attention in recent times, it can offer outstanding performance in face recognition systems, where the quantity of training data is amply large. We propose a Hyperspectral Face Recognition system using Firefly algorithm for band fusion and the Convolution Neural Network for classification. In addition to this, the present work is extended 11 exiting face recognition methods to perform Hyperspectral Face Recognition task. Thus the work has been framed as Hyperspectral Face Recognition problem to an image-set classification problem and assessment of the performance has been done on six state-of-the-art image-set problem techniques, and similarly it was examined on five state-of-the-art RGB and gray scale face recognition system, subsequently applied improved Firefly band selection algorithm on Hyperspectral Images to get appropriate band. Assessment with the eleven extended and five existing HSI Face Recognition system on two benchmark datasets (CMU-HSFD & UWA-HSFD) demonstrates that the proposed system overtakes all by a noteworthy margin. Lastly, we execute the band selection demonstration to get the novelty for most informative bands in Visible Near Infrared (VNIR). [ABSTRACT FROM AUTHOR] |
| Copyright of Microprocessors & Microsystems 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: 143553188 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rai%2C+Ashok+Kumar%22">Rai, Ashok Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akraiceg@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Senthilkumar%2C+Radha%22">Senthilkumar, Radha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22R%2C+Aswin+Kumar%22">R, Aswin Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Microprocessors+%26+Microsystems%22">Microprocessors & Microsystems</searchLink>. Jul2020, Vol. 76, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Pixels%22">Pixels</searchLink><br /><searchLink fieldCode="DE" term="%22Face%22">Face</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A Hyperspectral camera provides discriminating features for capturing human faces that cannot be obtained by any other imaging technique. Nevertheless, it has new issues comprising curse of dimensionality, physical parameter retrieval, fast computing and inter band misalignment. As a result of, the literature of Hyperspectral Face Recognition is more scanty and confined to improvised dimensionality reduction and minimization of wide-ranging bands, and thus the objective can be obtained by the use of the Convolution Neural Network (ConvNet). Since ConvNet is of great attention in recent times, it can offer outstanding performance in face recognition systems, where the quantity of training data is amply large. We propose a Hyperspectral Face Recognition system using Firefly algorithm for band fusion and the Convolution Neural Network for classification. In addition to this, the present work is extended 11 exiting face recognition methods to perform Hyperspectral Face Recognition task. Thus the work has been framed as Hyperspectral Face Recognition problem to an image-set classification problem and assessment of the performance has been done on six state-of-the-art image-set problem techniques, and similarly it was examined on five state-of-the-art RGB and gray scale face recognition system, subsequently applied improved Firefly band selection algorithm on Hyperspectral Images to get appropriate band. Assessment with the eleven extended and five existing HSI Face Recognition system on two benchmark datasets (CMU-HSFD & UWA-HSFD) demonstrates that the proposed system overtakes all by a noteworthy margin. Lastly, we execute the band selection demonstration to get the novelty for most informative bands in Visible Near Infrared (VNIR). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Microprocessors & Microsystems 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.micpro.2020.103096 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Human facial recognition software Type: general – SubjectFull: Pixels Type: general – SubjectFull: Face Type: general Titles: – TitleFull: Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rai, Ashok Kumar – PersonEntity: Name: NameFull: Senthilkumar, Radha – PersonEntity: Name: NameFull: R, Aswin Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 01419331 Numbering: – Type: volume Value: 76 Titles: – TitleFull: Microprocessors & Microsystems Type: main |
| ResultId | 1 |
