Identification and Extraction of Fingerprint Recognition by using Minutiae Features

Authors

  • Subhashini D. Department of Electronics and Communication, Annai College of Engineering and Technology, Kumbakonam Affiliated by Anna University, Chennai, India
  • Kiruthiga K Department of Electronics and Communication, Annai College of Engineering and Technology, Kumbakonam Affiliated by Anna University, Chennai, India
  • Niranjana S. Department of Electronics and Communication, Annai College of Engineering and Technology, Kumbakonam Affiliated by Anna University, Chennai, India
  • Mounika C. Department of Electronics and Communication, Annai College of Engineering and Technology, Kumbakonam Affiliated by Anna University, Chennai, India
  • Manikadan R. Department of Electronics and Communication, Annai College of Engineering and Technology, Kumbakonam Affiliated by Anna University, Chennai, India

DOI:

https://doi.org/10.37591/rtpc.v3i1.692

Abstract

Fingerprints are the ancient and most widely used form of biometric identification. Everyone has unique, irremovable fingerprints. Most of the Automatic Fingerprint Recognition Systems are based on local ridge and bifurcations as features known as minutiae, marking minutiae accurately are very important when it comes to matching of similar or non-similar fingerprints. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a large number of techniques like binarization, thinning, finding termination, orientation estimation, for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly. Here we proposed the binarized technique based on thinning to extracting minute features. This proposed system is going to simulated using Matlab R2013a software.

Keywords: Ridge, bifurcations, minutiae, binarized, MATLAB

Cite this Article
Subhashini et al. Identification and Extraction of Fingerprint Recognition by using Minutiae Features. Recent Trends in Parallel Computing. 2016; 3(1): 22–26p.

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Published

2016-04-30

Issue

Vol. 3 No. 1 (2016)

Section

Research Articles

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