Optimum geometric transformation and bipartite graph-based approach to sweat pore matching for biometric identification

Min Jae Kim, Whoi Yul Kim, Joonki Paik

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.

Original languageEnglish
Article number175
JournalSymmetry
Volume10
Issue number5
DOIs
StatePublished - 2018 May 1

Fingerprint

sweat
Geometric transformation
biometrics
Biometrics
Fingerprint
Bipartite Graph
Identification (control systems)
porosity
system identification
System Identification
Optical sensors
ridges
Ridge
Matching Algorithm
law (jurisprudence)
Pixels
Forensic Science
optical measuring instruments
Optical Sensor
Limit Distribution

Keywords

  • Biometric identification
  • Bipartite graph matching
  • Fingerprint recognition
  • Stable marriage problem
  • Sweat pore matching

Cite this

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title = "Optimum geometric transformation and bipartite graph-based approach to sweat pore matching for biometric identification",
abstract = "Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.",
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author = "Kim, {Min Jae} and Kim, {Whoi Yul} and Joonki Paik",
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Optimum geometric transformation and bipartite graph-based approach to sweat pore matching for biometric identification. / Kim, Min Jae; Kim, Whoi Yul; Paik, Joonki.

In: Symmetry, Vol. 10, No. 5, 175, 01.05.2018.

Research output: Contribution to journalArticle

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AU - Paik, Joonki

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AB - Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.

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