De-noising slap fingerprint images for accurate slap fingerprint segmentation

Ramaiah, N P and C, Krishna Mohan (2011) De-noising slap fingerprint images for accurate slap fingerprint segmentation. In: 10th International Conference on Machine Learning and Applications, ICMLA 2011, 18-21, December 2011, Honolulu, HI; United States.

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Fingerprints have unique properties like distinctiveness and persistence. Sometimes, fingerprint images can have some noisy data while capturing them using slap fingerprint scanners. This noise causes improper slap fingerprint segmentation due to which the performance of fingerprint matching decreases. The process of eliminating duplicates is called de-duplication which requires the plain quality fingerprints. While doing the segmentation of slap fingerprints, some of the fingerprint images are improperly segmented because of the noise present in the data. In this paper, an attempt is made to remove the noise present in the slap fingerprint data using binarization of slap fingerprint image, and region labeling of desired regions with 8-adjacency neighborhood for accurate slap fingerprint segmentation. Experimental results demonstrate that the fingerprint segmentation rate is improved from 78% to 99%.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: 8-adjacency neighborhood; de-duplication; slap fingerprint segmentation
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 29 Oct 2014 08:51
Last Modified: 01 Sep 2017 09:26
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