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

4 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
EditorsChu Kiong Loo, Kok Wai Wong, Keem Siah Yap, Kaizhu Huang, Andrew Teoh
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 C. K. Loo, K. W. Wong, K. S. Yap, K. Huang, & A. Teoh (Eds.), 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. editor / Chu Kiong Loo ; Kok Wai Wong ; Keem Siah Yap ; Kaizhu Huang ; Andrew Teoh. 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 CK Loo, KW Wong, KS Yap, K Huang & A Teoh (eds), 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. ed. / Chu Kiong Loo; Kok Wai Wong; Keem Siah Yap; Kaizhu Huang; Andrew Teoh. 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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Syarif MA, Ong TS, Teoh ABJ, Tee C. Improved biohashing method based on most intensive histogram block location. In Loo CK, Wong KW, Yap KS, Huang K, Teoh A, editors, 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)).