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Reseach Article

Fingerprint Segmentation Algorithms: A Literature Review

by Rohan Nimkar, Agya Mishra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 5
Year of Publication: 2014
Authors: Rohan Nimkar, Agya Mishra
10.5120/16590-6301

Rohan Nimkar, Agya Mishra . Fingerprint Segmentation Algorithms: A Literature Review. International Journal of Computer Applications. 95, 5 ( June 2014), 20-24. DOI=10.5120/16590-6301

@article{ 10.5120/16590-6301,
author = { Rohan Nimkar, Agya Mishra },
title = { Fingerprint Segmentation Algorithms: A Literature Review },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 5 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number5/16590-6301/ },
doi = { 10.5120/16590-6301 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:38.768186+05:30
%A Rohan Nimkar
%A Agya Mishra
%T Fingerprint Segmentation Algorithms: A Literature Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 5
%P 20-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. This paper review the different algorithms particularly used for fingerprint segmentation. Adaptive Total Variation Model, Directional Total Variation Model, method based on combination of ridge orientation and frequency features, method based on orientation field information combined with statistical characteristics of gray, Ridge Template Correlation, method using three pixel features(being the coherence, the mean and the variance) are discussed and compared based on their performance and concluded that Adaptive Total Variation Model and Directional Total Variation Model provides better results, so it will be good for the field of researches.

References
  1. Jiangyang Zhang, Rongjie Lai and C. C. Jay Kuo. 2012. Latent Fingerprint Segmentation with Adaptive Total Variation Model. IEEE.
  2. Jiangyang Zhang, Rongjie Lai and C. -C. Jay Kuo. 2012. Latent Fingerprint Detection and Segmentation with a Directional Total Variation Model. IEEE.
  3. Heeseung Choi, Maurilio Boaventura, Ines A. G. Boaventura and Anil K. Jain, Automatic Segmentation of Latent Fingerprints.
  4. Juntao Xue and Hongwei Li. 2012. Fingerprint image segmentation based on a combined method. IEEE.
  5. Nathan J. Short, Michael S. Hsiao, A. Lynn Abbott and Edward A. Fox. Latent Fingerprint Segmentation using Ridge Template Correlation.
  6. Asker M. Bazen and Sabih H. Gerez. 2001. Segmentation of Fingerprint Images, Workshop on Circuits, Systems and Signal Processing, Veldhoven. The Netherlands.
Index Terms

Computer Science
Information Sciences

Keywords

Image recognition fingerprint segmentation fingerprint image pre-processing total variation ridge orientation.