Fingerprint Recognition

ByVIKASH KUMAR

( 06-cse-20 )

Agenda

Introduction Fingerprint Classification Image Acquisition Image Processing Matching Paradigms Applications

A fingerprint is an individual characteristic; no two people have been found with the exact same fingerprint pattern.

A fingerprint pattern remains unchanged for the life of an individual; however, the print itself may change due to permanent scars and skin diseases.

Fingerprints have general characteristic ridge patterns that allow them to be systematically identified.

Introduction

Whorl ( )

Right Loop

Left Loop ( )

Tented Arch ( )

Arch ( )

Fingerprint Classification

Ellipse: A circular or oval shaped line-type which is generally found in the center of the fingerprint, it is generally found in the Whorl print pattern.

Bifurcation: It is the intersection of two or more line-types which converge or diverge.

Island: A line-type that stands alone.( i.e. does not touch another line-type)

Fingerprint Classification

Some other line types are:-

General Architecture

Fingerprint Acquisition

Acquisition of fingerprint Images was performed by two techniques

Off-line sensing -Although the first fingerprint scanners were introduced more than 30 years ago, still ink-technique is used in some applications

Live-scan sensing - Optical sensors Silicon based sensors Ultrasound sensors

The basic idea behind each capture approach is to measure in some way the physical difference between ridges and valleys

Fingerprint Image Segmentation

A Region of Interest (ROI) is useful to be recognized for each fingerprint image.

The image area without effective ridges and furrows is first discarded since it only holds background information.

Tessellation of Region of Interest

Fingerprint Image Enhancement

To make the image clearer for easy further operations.

For increasing the contrast between ridges and furrows and for connecting the false broken points of ridges.

Original Enhanced

Binarization The process of turning a grayscale image to a

black and white image. Transform the 8-bit Gray fingerprint image to a

1-bit image with 0-value for ridges and 1-value for furrows.

After the operation, ridges in the fingerprint are highlighted with black color while furrows are white.

Fingerprint Ridge Thinning

Reduces the width of the ridges to one pixel. Filling holes, removing small breaks, eliminating

bridges between ridges etc.

Thinned image

Minutia Marking

After the fingerprint ridge thinning, marking minutia points is relatively easy.

In general, for each 3x3 window, if the central pixel is 1 and has exactly 3 one-value neighbour (non-adjacent), then the central pixel is a ridge branch.

If the central pixel is 1 and has only 1 one-value neighbor, then the central pixel is a ridge ending.

0 1 00 1 01 0 1

0 0 00 1 00 0 1

Bifurcation Termination

Minutia Detection Method

Binarization Thinning Minutia DetectionAcquisition

Matching Paradigms Manual

Human experts use a combination of visual, textural, minutiae cues and experience for verification

Still used in the final stages of law enforcement applications

Image based Utilizes only visual appearance Requires the complete image to be stored

Texture based Treats the fingerprint as an oriented texture image Less accurate than minutiae based matchers since most

regions in the fingerprints carry low textural content Minutiae based

Uses the relative position of the minutiae points The most popular and accurate approach for verification Resembles manual approach very closely.

Minutiae Based Matching

Voting

Forensic matching

Identification of Criminals

Identification of missing children

Banking Security - ATM security, card transaction

Information System Security

National ID Systems

Passport control (INSPASS)

Prisoner, prison visitors, inmate control

Applications

Biometric Comparison