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

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

13 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 languageEnglish
Title of host publicationAI 2003
Subtitle of host publicationAdvances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings
EditorsTamas D. Gedeon, Lance Chun Che Fung, Tamas D. Gedeon
PublisherSpringer Verlag
Pages624-633
Number of pages10
ISBN (Print)9783540206460
Publication statusPublished - 2003 Jan 1
Event16th Australian Conference on Artificial Intelligence, AI 2003 - Perth, Australia
Duration: 2003 Dec 32003 Dec 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2903
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th Australian Conference on Artificial Intelligence, AI 2003
CountryAustralia
CityPerth
Period03/12/303/12/5

Fingerprint

Fingerprint
Image segmentation
Image Segmentation
Discrimination
Segmentation
Binary images
Image processing
Inspection
Post-processing
Experiments
Adaptive Thresholding
Binary Image
Counting
Image Processing
Enhancement
Necessary
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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.
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.",
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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.

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 proceedingConference contribution

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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)).