Model-based quality estimation of fingerprint images

Sanghoon Lee, Chulhan Lee, Jaihie Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Most automatic fingerprint identification systems identify a person using minutiae. However, minutiae depend almost entirely on the quality of the fingerprint images that are captured. Therefore, it is important that the matching step uses only reliable minutiae. The quality estimation algorithm deduces the availability of the extracted minutiae and allows for a matching step that will use only reliable minutiae. We propose a model-based quality estimation of fingerprint images. We assume that the ideal structure of a fingerprint image takes the shape of a sinusoidal wave consisting of ridges and valleys. To determine the quality of a fingerprint image, the similarity between the sinusoidal wave and the input fingerprint image is measured. The proposed method uses the 1-dimensional (1D) probability density function (PDF) obtained by projecting the 2-dimensional (2D) gradient vectors of the ridges and valleys in the orthogonal direction to the local ridge orientation. Quality measurement is then caculated as the similarity between the ID probability density functions of the sinusoidal wave and the input fingerprint image. In our experiments, we compared the proposed method and other conventional methods using FVC-2002 DB I, III procedures. The performance of verification and the separability between good and bad regions were tested.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Pages229-235
Number of pages7
Publication statusPublished - 2006 Jun 15
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 2006 Jan 52006 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Biometrics, ICB 2006
CountryChina
CityHong Kong
Period06/1/506/1/7

Fingerprint

Fingerprint
Model-based
Probability density function
Ridge
Identification (control systems)
Availability
Gradient vector
Separability
Estimation Algorithms
System Identification
Experiments
Deduce
Person
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lee, S., Lee, C., & Kim, J. (2006). Model-based quality estimation of fingerprint images. In Advances in Biometrics - International Conference, ICB 2006, Proceedings (pp. 229-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3832 LNCS).
Lee, Sanghoon ; Lee, Chulhan ; Kim, Jaihie. / Model-based quality estimation of fingerprint images. Advances in Biometrics - International Conference, ICB 2006, Proceedings. 2006. pp. 229-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{e9e62929649a462aa222eb33310886a1,
title = "Model-based quality estimation of fingerprint images",
abstract = "Most automatic fingerprint identification systems identify a person using minutiae. However, minutiae depend almost entirely on the quality of the fingerprint images that are captured. Therefore, it is important that the matching step uses only reliable minutiae. The quality estimation algorithm deduces the availability of the extracted minutiae and allows for a matching step that will use only reliable minutiae. We propose a model-based quality estimation of fingerprint images. We assume that the ideal structure of a fingerprint image takes the shape of a sinusoidal wave consisting of ridges and valleys. To determine the quality of a fingerprint image, the similarity between the sinusoidal wave and the input fingerprint image is measured. The proposed method uses the 1-dimensional (1D) probability density function (PDF) obtained by projecting the 2-dimensional (2D) gradient vectors of the ridges and valleys in the orthogonal direction to the local ridge orientation. Quality measurement is then caculated as the similarity between the ID probability density functions of the sinusoidal wave and the input fingerprint image. In our experiments, we compared the proposed method and other conventional methods using FVC-2002 DB I, III procedures. The performance of verification and the separability between good and bad regions were tested.",
author = "Sanghoon Lee and Chulhan Lee and Jaihie Kim",
year = "2006",
month = "6",
day = "15",
language = "English",
isbn = "3540311114",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "229--235",
booktitle = "Advances in Biometrics - International Conference, ICB 2006, Proceedings",

}

Lee, S, Lee, C & Kim, J 2006, Model-based quality estimation of fingerprint images. in Advances in Biometrics - International Conference, ICB 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3832 LNCS, pp. 229-235, International Conference on Biometrics, ICB 2006, Hong Kong, China, 06/1/5.

Model-based quality estimation of fingerprint images. / Lee, Sanghoon; Lee, Chulhan; Kim, Jaihie.

Advances in Biometrics - International Conference, ICB 2006, Proceedings. 2006. p. 229-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3832 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Model-based quality estimation of fingerprint images

AU - Lee, Sanghoon

AU - Lee, Chulhan

AU - Kim, Jaihie

PY - 2006/6/15

Y1 - 2006/6/15

N2 - Most automatic fingerprint identification systems identify a person using minutiae. However, minutiae depend almost entirely on the quality of the fingerprint images that are captured. Therefore, it is important that the matching step uses only reliable minutiae. The quality estimation algorithm deduces the availability of the extracted minutiae and allows for a matching step that will use only reliable minutiae. We propose a model-based quality estimation of fingerprint images. We assume that the ideal structure of a fingerprint image takes the shape of a sinusoidal wave consisting of ridges and valleys. To determine the quality of a fingerprint image, the similarity between the sinusoidal wave and the input fingerprint image is measured. The proposed method uses the 1-dimensional (1D) probability density function (PDF) obtained by projecting the 2-dimensional (2D) gradient vectors of the ridges and valleys in the orthogonal direction to the local ridge orientation. Quality measurement is then caculated as the similarity between the ID probability density functions of the sinusoidal wave and the input fingerprint image. In our experiments, we compared the proposed method and other conventional methods using FVC-2002 DB I, III procedures. The performance of verification and the separability between good and bad regions were tested.

AB - Most automatic fingerprint identification systems identify a person using minutiae. However, minutiae depend almost entirely on the quality of the fingerprint images that are captured. Therefore, it is important that the matching step uses only reliable minutiae. The quality estimation algorithm deduces the availability of the extracted minutiae and allows for a matching step that will use only reliable minutiae. We propose a model-based quality estimation of fingerprint images. We assume that the ideal structure of a fingerprint image takes the shape of a sinusoidal wave consisting of ridges and valleys. To determine the quality of a fingerprint image, the similarity between the sinusoidal wave and the input fingerprint image is measured. The proposed method uses the 1-dimensional (1D) probability density function (PDF) obtained by projecting the 2-dimensional (2D) gradient vectors of the ridges and valleys in the orthogonal direction to the local ridge orientation. Quality measurement is then caculated as the similarity between the ID probability density functions of the sinusoidal wave and the input fingerprint image. In our experiments, we compared the proposed method and other conventional methods using FVC-2002 DB I, III procedures. The performance of verification and the separability between good and bad regions were tested.

UR - http://www.scopus.com/inward/record.url?scp=33744958123&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33744958123&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33744958123

SN - 3540311114

SN - 9783540311119

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 229

EP - 235

BT - Advances in Biometrics - International Conference, ICB 2006, Proceedings

ER -

Lee S, Lee C, Kim J. Model-based quality estimation of fingerprint images. In Advances in Biometrics - International Conference, ICB 2006, Proceedings. 2006. p. 229-235. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).