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 |
| Publication status | Published - 2003 Jan 1 |
| 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 | Australia |
| City | Perth |
| Period | 03/12/3 → 03/12/5 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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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
TY - GEN
T1 - Fingerprint images segmentation using two stages coarse to fine discrimination technique
AU - Ong, T. S.
AU - Andrew, T. B.J.
AU - David, N. C.L.
AU - Sek, Y. W.
PY - 2003/1/1
Y1 - 2003/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=0347586940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347586940&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0347586940
SN - 9783540206460
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 624
EP - 633
BT - AI 2003
A2 - Gedeon, Tamas D.
A2 - Fung, Lance Chun Che
A2 - Gedeon, Tamas D.
PB - Springer Verlag
ER -