Fingerprint Introduction

Hi friends. This is the first time I am writing a blog. Hope this post will be a good source of information for blog readers as well as for the students doing their project on fingerprint recognition using matlab.   The main purpose of writing this blog is to present my project work before you. Recently I have worked on a Fingerprint Recognition technique using software called Matlab. Matlab is a tool for mathematical computation consisting of different toolbox out of which I have chosen image processing toolbox. 

Overview

Fingerprints have been accepted worldwide as a reliable biometric characteristic and used for person identification. The important features that separate fingerprint from others are its uniqueness, universality and stability. This has lead to the adoption of fingerprint recognition in the field of forensic, law enforcement and many other applications. The Law enforcement agencies and forensic departments routinely use fingerprint for the identification of criminals and victim. But they mostly rely on the minutia features present on fingerprint for recognition. However with the advancement in the quality of images it has become possible to reliably extract the pores for matching. Since the number of pores is more than the number of minutiae, pore matching alone has increased the matching accuracy of the fingerprint images.

What is fingerprint?

Fingerprint is composed of black and white line structure often called as ridges and valleys, out of which ridges are of key interest. Based on this ridges pattern and their appearance fingerprint features are generally classified into three levels. Level 1 feature is characterized by ridge flow pattern such as orientation field and singular points. The ridge pattern includes whorl, left loop, right loop, arch, tented arch and the singular points include core and delta points. Level 2 features also called as minutiae refer to ridge endings and bifurcations. A ridge ending is defined as a ridge point where ridge ends abruptly. A ridge bifurcation is defined as a ridge point where ridge diverges into branch ridges. Level 3 features include pores, dots, ridge contour and incipient ridge which are the fine details on fingerprint ridge.

Level 1 Features

 

Level 2 Features

Level 3 Features

Fingerprint recognition

It is the process of comparing two fingerprints to decide whether they are from same finger or not. Basically the recognition consists of two stages namely feature extraction and feature matching. Features can be level 2 features such as ridge ending and bifurcations or level 3 features such and pores and dots.

This is all about the basics of the fingerprint. In the next post I'll be discussing the different algorithms used for feature extraction and matching. Queries are most welcome. Feel free to ask and comment.