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.
|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|
|Number of pages||10|
|Publication status||Published - 2003|
|Event||16th Australian Conference on Artificial Intelligence, AI 2003 - Perth, Australia|
Duration: 2003 Dec 3 → 2003 Dec 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||16th Australian Conference on Artificial Intelligence, AI 2003|
|Period||03/12/3 → 03/12/5|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)