Security Systems using Fingerprint Biometry

Nishikant P. TaksandeIASRI, New Delhi, India

Contents Introduction Biometric Recognition Biometric Systems Fingerprints Fingerprint Recognition System Minutiae Extraction Minutiae Matching Applications Limitations Conclusions References

Introduction“Biometrics is the science of verifying

and establishing identity of an individual through physiological features or behavioral traits.”

Biometric is more about what you are than what you have or know

Password and tokens are not reliable

Introduction…Biometric has been used since 14th century in china

Everyone have unique physical or behavioral characteristics and so unique Identity

Enhanced convenience and augmented security measure

Biometric Recognition Biometric modalities

Physical Biometrics: Face, Fingerprints, Iris-scans, Hand

geometry

Behavioral Biometrics: Speech, Signature, and Keystroke dynamics

Chemical biometrics: Odor and the chemical composition of

human perspiration

Iris Iris is the area of the eye where the pigmented or coloured circle, usually brown, blue, rings the dark pupil of the eye

Image is typically captured using a noncontact imaging process

Iris should be at the predetermined distance from the focal plane of the camera

Iris portion

Face RecognitionMethod of acquiring face images is nonintrusive

Facial disguise is of concern in unattended recognition applications

It is challenging to develop techniques that can tolerate the effects of aging, facial expression, variations in the imaging environment

Hand and Finger Geometry

Features related to the human hand are relatively invariant and peculiar to an individual

System requires cooperation of the subject to capture frontal and side view images of the palm flatly placed on a panel with outstretched fingers

Only used for verification

Hand Vein Recognition

Back of a clenched fist used to determine hand vein structure

Systems for vein capture use inexpensive infra-red light emitting diodes

Voice Recognition

Voice capture is unobtrusive and only feasible applications requiring person recognition over a telephone

Voice signal available for recognition is typically degraded in quality by the microphone, communication channel

Voice is affected by factors such as a person’s health, stress and emotional state

SignatureThe way a person signs his name is known to be a characteristic of that individual

Signature is a behavioural biometric that changes over time

Professional forgers can reproduce signatures of others to fool the unskilled eye

Biometric Systems

An important issue in designing a practical biometric system is to determine how an individual is going to be recognized

Biometric systemEnrolmentVerification system Identification system

Enrolment

Feature Extraction

Template Creation

Data Storage

Template Identifier

SampleCapture Feature set

Subject Identifier

Enrolment Process

Verification

Feature Extraction Matching Data

Storage

Claimed Identity

SampleCapture Feature set

Subject Identifier

Verification Process

One Subjects template

Match or Non-Match

Identification

Feature Extraction

Pre-selectionand

MatchingData

Storage

SampleCapture Feature set

Subject Identifier

Identification Process

‘N’ Subject Templates

Subject Identified Or not Identified

Architecture

Biometric sensor Feature extraction

Database

Biometric sensor

Matching

Enrolment

Authentication Result

Feature extraction

General architecture of a biometric system

Comparison of Biometric Traits

Comparison of commonly used biometric traits

FingerprintSkin on human fingertips contains ridges and valleys which together forms distinctive patterns

These patterns are fully developed under pregnancy and are permanent throughout whole lifetime

No two persons have the same fingerprints

Automatization of the fingerprint recognition process turned out to be success in forensic applications

Fingerprint Patterns

Left Loop Right Loop

Fingerprint Patterns...

Whorl Arch Twin Loop

Fingerprint FeatureMinutiae Based Approach

Minutiae is the unique, measurable physical point at which ridge bifurcate or ends

Ridge Ending Ridge Bifurcation

Fingerprint Recognition System

Minutiae Extractor

Minutiae Matcher

Sensor

Architecture for Fingerprint Recognition System

Fingerprint Sensing and Storage

Off- line scan or live scan

A special kind of off-line images, extremely important in forensic applications, are the so-called latent fingerprints found at crime scenes

Live Scan Devices

Fingerprint image acquisition is the most critical step of an automated fingerprint authentication system

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

Physical principles like capacitive, optical and thermal are used

Optical Sensors FTIR-based Fingerprint Sensor

Frustrated Total Internal Reflection (FTIR)

Minutia Extraction

Pre-Processing

Fingerprint Image Enhancement

Fingerprint Image enhancement is to make the image clearer for easy further operations

Increase the contrast between ridges and furrows and connect the broken points

Minutia Extraction...

Histogram Equalization

Histogram equalization is to expand the pixel value distribution of an image so as to increase the perceptional information

The Original Histogram of a fingerprint image

Histogram after histogram equalization

Minutia Extraction...

Histogram Equalization

Original Image Enhanced Image after Histogram Equalization

Minutia Extraction...

Fingerprint Image Binarization

8-bit Gray fingerprint image transform to a 1-bit image with 0-value for ridges and 1-value for furrows

Ridges in the fingerprint are highlighted with black colour while furrows are white

Minutia Extraction...

Fingerprint Image Segmentation

Region of Interest (ROI) is useful to be recognized for each fingerprint imageTwo step method is based on Morphological methods

ROI extraction by Morphological operations

Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted

Minutia Extraction...ROI extraction by Morphological operations

Original Image Area

ROI + BoundAfter OPEN operation

After CLOSE operation

Minutia Extraction...

Minutia Extraction

Fingerprint Ridge Thinning

Ridge Thinning is to eliminate the redundant pixels of ridges

Minutia Marking

If the central pixel is 1 and has exactly 3 one-value neighbours, then the central pixel is a ridge branch

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

Minutia Extraction...

Minutia Marking

Bifurcation Termination A fingerprint after minutiae extraction

Minutia Extraction...

Constellation Creation Technique

Minutia Extraction...

Constellation Creation Technique

Minutia Extraction...

Constellation Creation Technique

Minutia Extraction...

Constellation Creation Technique

The Matching Module

The matching module use a pre-processed pattern composed by the fingerprint, it’s minutia-file and it’s constellations,

These patterns are extracted from enrolled templates

Feature extraction is done then its patterns are compared to the reference ones

Comparison between the two fingerprints Constellation matchingMinutiae matching

Constellation Matching

When a new fingerprint is scanned it is passed by the feature extraction module, a set of constellations and their respective parameters are created

Comparison with the genuine constellations set extracted during system enrolment

F1 = {C1.0, C1.1, C1.2, C1.3} F2 = {C2.0, C2.1, C2.3, C2.4, C2.5}

Euclidean distance between the point patterns of constellations centres

Constellation Matching

Rejects a fingerprint template No constellation is matched D = 0A unique constellation that includes less than 15 minutiae is matched D = 0

Co1.0

Co1.1

Co1.2Co1.3

Co2.2

Co2.1

Co2.3

Co2.0

Fingerprint constellation matching

Minutiae Matching

Minutiae matching are performed within a constellation

The minutiae matching are used as a second level verification

minutiae matching algorithm proceeds as follow

Associate minutiae pointsCompute distance between minutiaeDecide

Minutiae MatchingFlow Chart

Start

Decision=reject

ConstellationMatching

Minutiae Matching

Decision=Accept

END

Fingerprint and constellation

files

CTH

MTH

D<CTH

D<MTH

NO

NO

Applications of Fingerprint Recognition System

Physical Access Control

Logical Access Control

Transaction Authentication

Device Access Control

Time and Attendance

Civil Identification

Forensic Identification

Limitations

Dirt , grime and wounds

Placement of finger

Too big database to process

Liveness important

In case of permanent finger injury

Barriers to Adoption

Business value is difficult to quantify in terms of return on investment

Fingerprint recognition systems, being an emerging technology, is sometimes confronted with unrealistic performance

The quality varies quite dramatically from one application to another and from one vendor to another

Several fingerprint system vendors are not financially stable

Conclusions

Most highly used methods for human recognition

Fingerprint system strongly relies on the precision

obtained in the minutia extraction process

Industrial commitment led to next generation

fingerprint technology

It has got broad acceptance from forensic to

handheld devices

Used for securing international borders

References

[1] FBI Fingerprint guide. Available online at

http://www.fbi.gov/hq/cjisd/takingfps.html

[2] Fingerprint. Available online at

http://en.wikipedia.org/wiki/Fingerprint

[3] Fingerprint recognition. Available online at

http://en.wikipedia.org/wiki/Fingerprint recognition

[4] Goyal, A., Study of Fingerprints Minutia Extraction and

matching Technique. Available online at

http://www.advancedcenterpunjabi.org/MTechP/Anjna

%20Goyal_M.Tech(CS).pdf

References...

[5] Human Fingerprints. Available online at

http://ww.fingerprinting.com/human-fingerprints.php

[6] Maltoni, D., Maio, D., Jain, A., Prabhakar, S.,(2009),

Handbook of Fingerprint Recognition, second edition,

Springer Limited.

[7] Murmu, N., Otti, A., Fingerprint recognition. Available online

at http://ethesis.nitrkl.ac.in/955/

[8] Rokbani, N., Alimi, A., Fingerprint identification using

minutiae constellation matching. Avialable online at

http://www.regim.org/publications/conferences/2005/2005_

MCCSIS_RA.pdf

THANK YOU