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

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

Research output: Contribution to journalArticle

21 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 languageEnglish
Article number7379005
Pages (from-to)1415-1428
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume46
Issue number10
DOIs
Publication statusPublished - 2016 Oct 1

Fingerprint

Authentication
Biometrics
Principal component analysis
Cryptography

All Science Journal Classification (ASJC) codes

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

Cite this

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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.",
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Generating Fixed-Length Representation from Minutiae Using Kernel Methods for Fingerprint Authentication. / Jin, Zhe; Lim, Meng Hu; Teoh, Beng Jin; Goi, Bok Min; Tay, Yong Haur.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 46, No. 10, 7379005, 01.10.2016, p. 1415-1428.

Research output: Contribution to journalArticle

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