Fingerprint images segmentation using two stages coarse to fine discrimination technique

T. S. Ong; T. B.J. Andrew; N. C.L. David; Y. W. Sek

doi:10.1007/978-3-540-24581-0_53

Fingerprint images segmentation using two stages coarse to fine discrimination technique

T. S. Ong, T. B.J. Andrew, N. C.L. David, Y. W. Sek

Department of Electrical and Electronic Engineering

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

14 Citations (Scopus)

Abstract

Segmentation of fingerprint image is necessary to reduce the size of the input data, eliminating undesired background, which is the noisy and smudged area in favor of the central part of the fingerprint. In this paper, an algorithm for the segmentation which uses two stages coarse to fine approach is presented. The coarse segmentation will be performed at first using the orientation certainty values that derived from the blockwise directional field of the fingerprint image. The coarse segmented image will be carry on to the second stage which consist Fourier based enhancement and adaptive thresholding. Orientation certainty values of the resultant binarized image are calculated once again to perform the fine segmentation. Finally, binary image processing is applied as postprocessing to further reduce the segmentation error. Visual inspection shows that the proposed method produce accurate segmentations result. The algorithm is also evaluated by counting the number of false and missed detected center points and compare with the fingerprint image which have no segmentation and with the proposed method without postprocessing. Experiments show that the proposed segmentation method perform well than others.

Original language	English
Title of host publication	AI 2003
Subtitle of host publication	Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings
Editors	Tamas D. Gedeon, Lance Chun Che Fung, Tamas D. Gedeon
Publisher	Springer Verlag
Pages	624-633
Number of pages	10
ISBN (Print)	9783540206460
DOIs	https://doi.org/10.1007/978-3-540-24581-0_53
Publication status	Published - 2003
Event	16th Australian Conference on Artificial Intelligence, AI 2003 - Perth, Australia Duration: 2003 Dec 3 → 2003 Dec 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2903
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	16th Australian Conference on Artificial Intelligence, AI 2003
Country/Territory	Australia
City	Perth
Period	03/12/3 → 03/12/5

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-24581-0_53

Cite this

Ong, T. S., Andrew, T. B. J., David, N. C. L., & Sek, Y. W. (2003). Fingerprint images segmentation using two stages coarse to fine discrimination technique. In T. D. Gedeon, L. C. C. Fung, & T. D. Gedeon (Eds.), AI 2003: Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings (pp. 624-633). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2903). Springer Verlag. https://doi.org/10.1007/978-3-540-24581-0_53

Ong, T. S. ; Andrew, T. B.J. ; David, N. C.L. ; Sek, Y. W. / Fingerprint images segmentation using two stages coarse to fine discrimination technique. AI 2003: Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings. editor / Tamas D. Gedeon ; Lance Chun Che Fung ; Tamas D. Gedeon. Springer Verlag, 2003. pp. 624-633 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Segmentation of fingerprint image is necessary to reduce the size of the input data, eliminating undesired background, which is the noisy and smudged area in favor of the central part of the fingerprint. In this paper, an algorithm for the segmentation which uses two stages coarse to fine approach is presented. The coarse segmentation will be performed at first using the orientation certainty values that derived from the blockwise directional field of the fingerprint image. The coarse segmented image will be carry on to the second stage which consist Fourier based enhancement and adaptive thresholding. Orientation certainty values of the resultant binarized image are calculated once again to perform the fine segmentation. Finally, binary image processing is applied as postprocessing to further reduce the segmentation error. Visual inspection shows that the proposed method produce accurate segmentations result. The algorithm is also evaluated by counting the number of false and missed detected center points and compare with the fingerprint image which have no segmentation and with the proposed method without postprocessing. Experiments show that the proposed segmentation method perform well than others.",

author = "Ong, {T. S.} and Andrew, {T. B.J.} and David, {N. C.L.} and Sek, {Y. W.}",

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Ong, TS, Andrew, TBJ, David, NCL & Sek, YW 2003, Fingerprint images segmentation using two stages coarse to fine discrimination technique. in TD Gedeon, LCC Fung & TD Gedeon (eds), AI 2003: Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2903, Springer Verlag, pp. 624-633, 16th Australian Conference on Artificial Intelligence, AI 2003, Perth, Australia, 03/12/3. https://doi.org/10.1007/978-3-540-24581-0_53

Fingerprint images segmentation using two stages coarse to fine discrimination technique. / Ong, T. S.; Andrew, T. B.J.; David, N. C.L.; Sek, Y. W.

AI 2003: Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings. ed. / Tamas D. Gedeon; Lance Chun Che Fung; Tamas D. Gedeon. Springer Verlag, 2003. p. 624-633 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2903).

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

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AU - David, N. C.L.

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PY - 2003

Y1 - 2003

N2 - Segmentation of fingerprint image is necessary to reduce the size of the input data, eliminating undesired background, which is the noisy and smudged area in favor of the central part of the fingerprint. In this paper, an algorithm for the segmentation which uses two stages coarse to fine approach is presented. The coarse segmentation will be performed at first using the orientation certainty values that derived from the blockwise directional field of the fingerprint image. The coarse segmented image will be carry on to the second stage which consist Fourier based enhancement and adaptive thresholding. Orientation certainty values of the resultant binarized image are calculated once again to perform the fine segmentation. Finally, binary image processing is applied as postprocessing to further reduce the segmentation error. Visual inspection shows that the proposed method produce accurate segmentations result. The algorithm is also evaluated by counting the number of false and missed detected center points and compare with the fingerprint image which have no segmentation and with the proposed method without postprocessing. Experiments show that the proposed segmentation method perform well than others.

AB - Segmentation of fingerprint image is necessary to reduce the size of the input data, eliminating undesired background, which is the noisy and smudged area in favor of the central part of the fingerprint. In this paper, an algorithm for the segmentation which uses two stages coarse to fine approach is presented. The coarse segmentation will be performed at first using the orientation certainty values that derived from the blockwise directional field of the fingerprint image. The coarse segmented image will be carry on to the second stage which consist Fourier based enhancement and adaptive thresholding. Orientation certainty values of the resultant binarized image are calculated once again to perform the fine segmentation. Finally, binary image processing is applied as postprocessing to further reduce the segmentation error. Visual inspection shows that the proposed method produce accurate segmentations result. The algorithm is also evaluated by counting the number of false and missed detected center points and compare with the fingerprint image which have no segmentation and with the proposed method without postprocessing. Experiments show that the proposed segmentation method perform well than others.

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PB - Springer Verlag

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Y2 - 3 December 2003 through 5 December 2003

ER -

Ong TS, Andrew TBJ, David NCL, Sek YW. Fingerprint images segmentation using two stages coarse to fine discrimination technique. In Gedeon TD, Fung LCC, Gedeon TD, editors, AI 2003: Advances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings. Springer Verlag. 2003. p. 624-633. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-24581-0_53

Fingerprint images segmentation using two stages coarse to fine discrimination technique

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