Improved biohashing method based on most intensive histogram block location

Munalih Ahmad Syarif, Thian Song Ong, Andrew Beng Jin Teoh, Connie Tee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Biohashing is a promising cancellable biometrics method. However, it suffers from a problem known as ‘stolen token scenario’. The performance of the biometric system drops significantly if the Biohashing private token is stolen. To solve this problem, this paper proposes a new method termed as Most Intensive Histogram Block Location (MIBL) to extract additional information of the p-th best gradient magnitude. Experimental analysis shows that the proposed method is able to solve the stolen token problem with error equal rates as low as 1.46% and 7.27% when the stolen token scenario occurred for both FVC2002 DB1 and DB2 respectively.

Original languageEnglish
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
PublisherSpringer Verlag
Pages644-652
Number of pages9
ISBN (Electronic)9783319126425
Publication statusPublished - 2014 Jan 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Biometrics
Histogram
Scenarios
Experimental Analysis
Gradient

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Syarif, M. A., Ong, T. S., Teoh, A. B. J., & Tee, C. (2014). Improved biohashing method based on most intensive histogram block location. In Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings (pp. 644-652). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836). Springer Verlag.
Syarif, Munalih Ahmad ; Ong, Thian Song ; Teoh, Andrew Beng Jin ; Tee, Connie. / Improved biohashing method based on most intensive histogram block location. Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Springer Verlag, 2014. pp. 644-652 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Syarif, MA, Ong, TS, Teoh, ABJ & Tee, C 2014, Improved biohashing method based on most intensive histogram block location. in Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8836, Springer Verlag, pp. 644-652.

Improved biohashing method based on most intensive histogram block location. / Syarif, Munalih Ahmad; Ong, Thian Song; Teoh, Andrew Beng Jin; Tee, Connie.

Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Springer Verlag, 2014. p. 644-652 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Biohashing is a promising cancellable biometrics method. However, it suffers from a problem known as ‘stolen token scenario’. The performance of the biometric system drops significantly if the Biohashing private token is stolen. To solve this problem, this paper proposes a new method termed as Most Intensive Histogram Block Location (MIBL) to extract additional information of the p-th best gradient magnitude. Experimental analysis shows that the proposed method is able to solve the stolen token problem with error equal rates as low as 1.46% and 7.27% when the stolen token scenario occurred for both FVC2002 DB1 and DB2 respectively.

AB - Biohashing is a promising cancellable biometrics method. However, it suffers from a problem known as ‘stolen token scenario’. The performance of the biometric system drops significantly if the Biohashing private token is stolen. To solve this problem, this paper proposes a new method termed as Most Intensive Histogram Block Location (MIBL) to extract additional information of the p-th best gradient magnitude. Experimental analysis shows that the proposed method is able to solve the stolen token problem with error equal rates as low as 1.46% and 7.27% when the stolen token scenario occurred for both FVC2002 DB1 and DB2 respectively.

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Syarif MA, Ong TS, Teoh ABJ, Tee C. Improved biohashing method based on most intensive histogram block location. In Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Springer Verlag. 2014. p. 644-652. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).