Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication

Zhe Jin; Meng Hu Lim; Andrew Beng Jin Teoh; Bok Min Goi; Yong Haur Tay

doi:10.1109/TSMC.2015.2499725

Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication

Zhe Jin, Meng Hu Lim, Andrew Beng Jin Teoh, Bok Min Goi, Yong Haur Tay

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

57 Citations (Scopus)

Abstract

The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: 1) minutiae descriptor extraction; 2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; 3) a dynamic feature binarization; and 4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.

Original language	English
Article number	7379005
Pages (from-to)	1415-1428
Number of pages	14
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	46
Issue number	10
DOIs	https://doi.org/10.1109/TSMC.2015.2499725
Publication status	Published - 2016 Oct

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea under Grant 2013006574, in part by the Universiti Tunku Abdul Rahman Research Fund (UTARRF) under Grant IPSR/RMC/UTARRF/2013-C2/G04, in part by the Anhui Provincial Project of Natural Science under Grant KJ2014A095, and in part by the eScience, MOSTI, Malaysia, under Grant 01-02-11-SF0201.

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TSMC.2015.2499725

Cite this

@article{ee52395a395743e39e198004ab8a985b,

title = "Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication",

abstract = "The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: 1) minutiae descriptor extraction; 2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; 3) a dynamic feature binarization; and 4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.",

author = "Zhe Jin and Lim, {Meng Hu} and Teoh, {Andrew Beng Jin} and Goi, {Bok Min} and Tay, {Yong Haur}",

note = "Funding Information: This work was supported in part by the National Research Foundation of Korea under Grant 2013006574, in part by the Universiti Tunku Abdul Rahman Research Fund (UTARRF) under Grant IPSR/RMC/UTARRF/2013-C2/G04, in part by the Anhui Provincial Project of Natural Science under Grant KJ2014A095, and in part by the eScience, MOSTI, Malaysia, under Grant 01-02-11-SF0201. Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2016",

month = oct,

doi = "10.1109/TSMC.2015.2499725",

language = "English",

volume = "46",

pages = "1415--1428",

journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",

issn = "2168-2216",

publisher = "IEEE Advancing Technology for Humanity",

number = "10",

}

TY - JOUR

T1 - Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication

AU - Jin, Zhe

AU - Lim, Meng Hu

AU - Teoh, Andrew Beng Jin

AU - Goi, Bok Min

AU - Tay, Yong Haur

N1 - Funding Information: This work was supported in part by the National Research Foundation of Korea under Grant 2013006574, in part by the Universiti Tunku Abdul Rahman Research Fund (UTARRF) under Grant IPSR/RMC/UTARRF/2013-C2/G04, in part by the Anhui Provincial Project of Natural Science under Grant KJ2014A095, and in part by the eScience, MOSTI, Malaysia, under Grant 01-02-11-SF0201. Publisher Copyright: © 2016 IEEE.

PY - 2016/10

Y1 - 2016/10

N2 - The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: 1) minutiae descriptor extraction; 2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; 3) a dynamic feature binarization; and 4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.

AB - The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: 1) minutiae descriptor extraction; 2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; 3) a dynamic feature binarization; and 4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.

UR - http://www.scopus.com/inward/record.url?scp=84988602855&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84988602855&partnerID=8YFLogxK

U2 - 10.1109/TSMC.2015.2499725

DO - 10.1109/TSMC.2015.2499725

M3 - Article

AN - SCOPUS:84988602855

SN - 2168-2216

VL - 46

SP - 1415

EP - 1428

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

IS - 10

M1 - 7379005

ER -

Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this