Improved biohashing method based on most intensive histogram block location

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

doi:10.1007/978-3-319-12643-2_78

Improved biohashing method based on most intensive histogram block location

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 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 language	English
Title of host publication	Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
Editors	Chu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
Publisher	Springer Verlag
Pages	644-652
Number of pages	9
ISBN (Electronic)	9783319126425
DOIs	https://doi.org/10.1007/978-3-319-12643-2_78
Publication status	Published - 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8836
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-12643-2_78

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. S. Yap, K. W. Wong, A. Teoh, & K. Huang (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. https://doi.org/10.1007/978-3-319-12643-2_78

Syarif, Munalih Ahmad ; Ong, Thian Song ; Teoh, Andrew Beng Jin et al. / Improved biohashing method based on most intensive histogram block location. Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. editor / Chu Kiong Loo ; Keem Siah Yap ; Kok Wai Wong ; Andrew Teoh ; Kaizhu Huang. 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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abstract = "Biohashing is a promising cancellable biometrics method. However, it suffers from a problem known as {\textquoteleft}stolen token scenario{\textquoteright}. 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.",

author = "Syarif, {Munalih Ahmad} and Ong, {Thian Song} and Teoh, {Andrew Beng Jin} and Connie Tee",

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year = "2014",

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series = "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, KS Yap, KW Wong, A Teoh & K Huang (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. https://doi.org/10.1007/978-3-319-12643-2_78

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

Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. ed. / Chu Kiong Loo; Keem Siah Yap; Kok Wai Wong; Andrew Teoh; Kaizhu Huang. 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 proceeding › Conference contribution

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AU - Syarif, Munalih Ahmad

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PY - 2014

Y1 - 2014

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 Loo CK, Yap KS, Wong KW, Teoh A, Huang K, 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)). doi: 10.1007/978-3-319-12643-2_78

Improved biohashing method based on most intensive histogram block location

Abstract

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