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IJML 2019 Vol.9(1): 51-56
doi: 10.18178/ijmlc.2019.9.1.764

An Online Integrated Fingerprint Image System

Ching-Yu Huang1 , Limin Liu2 , Yui-Liang Chen2

  • 1Kean University, Union, NJ 07083, USA.
  • 2Shih Hsin University, Taipei, Taiwan.

Abstract

Fingerprint analysis is one of the most commonly-used biometrics technologies, primarily used in forensic science to identify people. Due to various reasons, most fingerprint related studies are conducted in an isolated environment which makes it hard for researchers to collaborate across institutes. Web-based client/server applications have become an important component for international collaboration because such an application allows users from different locations to execute the same program on the same data. Such systems can be quickly developed and are easy to use. In this paper, a 3-tier integrated online fingerprint system was developed to manage and process fingerprint images in real-time. This system allows users all over the world to access the public NIST-14 fingerprint dataset and collaborate with other researchers to conduct the research task. The front-end of the system is an advanced web-based interface that allows users to view fingerprint images in different resolution, singular points, enter, and update the location of singular points. This system can also preserve the entered information in the back-end database.

Keywords

  • Classification
  • database
  • fingerprint
  • online image processing
764-P0011

How to Cite

Copied

Ching-Yu Huang, Limin Liu, and Yui-Liang Chen, "An Online Integrated Fingerprint Image System," International Journal of Machine Learning, vol. 9, no. 1, pp. 51-56, 2019. https://doi.org/10.18178/ijmlc.2019.9.1.764

Copyright & License

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD