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Reducing dense local feature key-points for faster iris recognition.

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Title: Reducing dense local feature key-points for faster iris recognition.
Authors: Sahu, Beeren1 beeren4u@gmail.com, Kumar Sa, Pankaj1 pankajksa@nitrkl.ac.in, Bakshi, Sambit1 bakshisambit@nitrkl.ac.in, Sangaiah, Arun Kumar2 sarunkumar@vit.ac.in
Source: Computers & Electrical Engineering. Aug2018, Vol. 70, p939-949. 11p.
Subjects: Iris recognition, Biometry, Databases, Feature extraction, Imaging systems
Abstract: Abstract Iris recognition has gained much attention in research and commercialization during the last decade. For a large population, the matching time of iris biometric system is much slower than the requirement. More the enrolled population size, higher the identification delay. To combat the delay without compromising accuracy of the system, the proposed method introduces a density-based spatial clustering and key point reduction to be applied on Phase Intensive Local Pattern (PILP) based dense feature extracted from the image. The reduction technique can also work with other dense local features. The reduction method is investigated whether it harms the accuracy of iris biometric system with respect to PILP. Widely used databases: BATH and CASIAv3 are used for experimentation. The technique is found successful in reducing representative key-points, thereby speeding up the match time up to five times. This improvement in 1:1 match-time is significant, and becomes more meaningful in identification for a large population. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electrical Engineering 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
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Header DbId: egs
DbLabel: Engineering Source
An: 131731705
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="AR" term="%22Sahu%2C+Beeren%22">Sahu, Beeren</searchLink><relatesTo>1</relatesTo><i> beeren4u@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar+Sa%2C+Pankaj%22">Kumar Sa, Pankaj</searchLink><relatesTo>1</relatesTo><i> pankajksa@nitrkl.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bakshi%2C+Sambit%22">Bakshi, Sambit</searchLink><relatesTo>1</relatesTo><i> bakshisambit@nitrkl.ac.in</i><br /><searchLink fieldCode="AR" term="%22Sangaiah%2C+Arun+Kumar%22">Sangaiah, Arun Kumar</searchLink><relatesTo>2</relatesTo><i> sarunkumar@vit.ac.in</i>
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electrical+Engineering%22">Computers & Electrical Engineering</searchLink>. Aug2018, Vol. 70, p939-949. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Iris+recognition%22">Iris recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink>
– Name: Abstract
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  Data: Abstract Iris recognition has gained much attention in research and commercialization during the last decade. For a large population, the matching time of iris biometric system is much slower than the requirement. More the enrolled population size, higher the identification delay. To combat the delay without compromising accuracy of the system, the proposed method introduces a density-based spatial clustering and key point reduction to be applied on Phase Intensive Local Pattern (PILP) based dense feature extracted from the image. The reduction technique can also work with other dense local features. The reduction method is investigated whether it harms the accuracy of iris biometric system with respect to PILP. Widely used databases: BATH and CASIAv3 are used for experimentation. The technique is found successful in reducing representative key-points, thereby speeding up the match time up to five times. This improvement in 1:1 match-time is significant, and becomes more meaningful in identification for a large population. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Computers & Electrical Engineering 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:
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      – Type: doi
        Value: 10.1016/j.compeleceng.2017.12.048
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 939
    Subjects:
      – SubjectFull: Iris recognition
        Type: general
      – SubjectFull: Biometry
        Type: general
      – SubjectFull: Databases
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Imaging systems
        Type: general
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      – TitleFull: Reducing dense local feature key-points for faster iris recognition.
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            NameFull: Sahu, Beeren
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            NameFull: Kumar Sa, Pankaj
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            NameFull: Bakshi, Sambit
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            NameFull: Sangaiah, Arun Kumar
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            – D: 01
              M: 08
              Text: Aug2018
              Type: published
              Y: 2018
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              Value: 70
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