Biometric seminar proma

Introduction to Computer vision & Biometrics Usefulness of Biometric Authentication Categories of Biometrics Characteristic Working principle Fingerprint recognition Fingerprint recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Fingerprint Recognition Gait analysis Gait analysis Gait analysis Applications Performance Advantage & Disadvantage

Computer Vision & Biometrics

Proma Goswami

Roll No:97/IT/130005

University Of Calcutta

21th March, 2014

Proma Goswami , Roll No:97/IT/130005 1


Contents

Introduction to Computer vision & Biometrics

Categories of Biometrics

Vision based Biometrics

Basic characteristics of Biometric Technologies

Working principle

Fingerprint recognition

Gait analysis

Applications

Biometric system performance

Advantage & Disadvantage of these Biometric methods



Definitions

Computer vision is a field that includes methods for acquiring, processing,analyzing, and understanding images and, in general, high-dimensionaldata from the real world in order to produce numerical or symbolicinformation, e.g., in the forms of decisions

Biometric is the science and technology of measuring and analyzingbiological data. In information technology, biometrics refers totechnologies that measure and analyze human body characteristics, suchas DNA, fingerprints, eye retinas and irises, voice patterns, facialpatterns,gait and hand geometry for authentication purposes.



Why we need Biometrics

The Security field uses 3 types of authentication

Something you know:- a password, PIN, or piece of personal information(such as your mother’s maiden name)

Something you have:- a card key, smart card, or token(like a Secure IDcard)

Something you are :- a biometric.

In between these biometrics is the most secure and convenient authenticationtool. It can’t be borrowed, stolen, or forgotten, and forging one is practicallyimpossible. (Replacement part surgery, by the way, is outside the scope of thisintroduction.)

Important Statistics:-

The average adult working in a large business has 12 passwords toremember, and spends nearly a week in every year logging into systems.

The average cost to a large company for every password lost is $16



Categories & Vision based Biometrics

Vision Based Biometrics

How Computer vision isapplied in Biometrictechniques?

DNA,Hair,Speech,Odorrecognition areautomated method butnot vision basedvision-based biometrics- those that use imagesensors and algorithmsderived from machinevisionAs vision-basedBiometrics we willdiscuss Fingerprint,Gait analysis



Basic characteristics of Biometric Technologies

Universality:Every person should have the characteristic. People who aremute or without a fingerprint will need to be accommodated in some way.

Uniqueness: Generally, no two people have identical characteristics.However, identical twins are hard to distinguish.

Permanence: The characteristics should not vary with time. A person’sface, for example, may change with age.

Collectibility: The characteristics must be easily collectible andmeasurable.

Performance: The method must deliver accurate results under variedenvironmental circumstances.

Acceptability: The general public must accept the sample collectionroutines. Nonintrusive methods are more acceptable.

Circumvention: The technology should be difficult to deceive



Working principle

Biometric devices consist of a reader or scanning device software thatconverts the gathered information into digital form, and a database thatstores the biometric data with comparison with existing recordsModes:

Enrollment Mode:A sample of the biometric trait is captured, processed by a computer, and storedfor later comparison

Verification Mode:In this mode biometric system authenticates a persons claimed identity fromtheir previously enrolled pattern.



Fingerprint Recognition

Fingerprint Patterns:- Basically there are 6 classes of patterns



Fingerprint recognition

For fingerprint Recognition welook at:

Crossover:-Two ridgescross each other

Core:- Center

Bifurcation:-Ridgeseparates

Ridge ending:-End point

Pore:-Human pore

Delta:-Space betweenridges

Various Fingre print matching mechanism

Corelation based method

Minutiae based method

Ridge pattern based method



A MINUTIAE-BASED MATCHING ALGORITHMS in FingerprintRecognition Systems

What is Minuatiae?

Ridge endings ,bifurcations on a persons finger are used to plot points know asMinutiae

Minutiae matching essentially consist of finding the best alignment between thetemplate (set of minutiae in the database) and a subset of minutiae in theinput fingerprint, through a geometric transformation.There are two processes in this algorithm

Feature Extraction

Matcher







Feature Extraction Two approaches of minutia extraction process can befound. The simplest and most used method is based on binarization andridge thinning stage. Due to a problem of the false minutiae introduced bythinning, some authors proposed direct grey-scale minutiae extraction.Ridge Thinning Method The most commonly used method of minutiaeextraction is the Crossing Number (CN) concept. There are 2 steps:-

The first step is to binarizateThin the ridges, so that they are single pixel wide.(shown in the figure)

Figure : Fingerprint image a) binarization and b) skeletonization.Proma Goswami , Roll No:97/IT/130005 12



The minutiae points are determined by scanning the local neighbourhood ofeach pixel in the ridge thinned image, using a 33 window (Figure shown below).

Figure : a) Ridge ending and b) bifurcation in c)3× 3 window.




Now filter the image to check whether the image is in database and the imagewe get matched or not?

Figure : Procedure of Finger print recognition




Feature Extractor actually done these 3 steps

Capture Image

Enhance Ridge

Extract Minutiae




Matcher

Used to match fingerprint

Trade-off between speed and performance

Group minutiae and categorize by typeLarge number of certain type can result in faster searches



Security

Accuracy97% will return correct results100% deny intruders

Image is discardedCannot reconstruct the fingerprint from data



Gait analysis

Another new approach is biometric recognition system based on gait

Gait is a persons manner of walking

Biometric gait recognition refers to verifying and/or identifying personsusing their walking style

Human recognition based on gait is relatively recent,

Various Gait analysis mechanism

MV based method: gait is captured using a video-camera from distance

FS based: a set of sensors or force plates are installed on the flors andactivate when a person walks

WS based: In WS-based gait recognition, gait is collected using body wornmotion record- ing (MR) sensors

Here we shall discuss about MV based gait analysis



MV based Gait recognition

BenAbdelkader et al. (2002) used stride and cadence for personidentification

Johnson and Bobick et al. (2001) extracted static body parameters suchas the height, the distance between head and pelvis, the maximumdistance between pelvis and feet

Most of the MV based gait recognition algorithms are based on the humansilhouette

What is Human Silhouette?

Figure : Example of gait detection. (a) Background image; (b) Original image; and(c) Extracted silhouette



MV based Gait recognition

BenAbdelkader et al. (2002) used stride and cadence for personidentification

Johnson and Bobick et al. (2001) extracted static body parameters suchas the height, the distance between head and pelvis, the maximumdistance between pelvis and feet

Most of the MV based gait recognition algorithms are based on the humansilhouette

What is Human Silhouette?

Figure : Example of gait detection. (a) Background image; (b) Original image; and(c) Extracted silhouette



Silhouette based Gait Feature extraction & Recognition system

Center of Mass: At the time of walking, thecenter of mass location is noted and extracted

step size length : Boundary box mechanism isused to measure step length

Figure : Boundary box to measure width

Gait cycle length: when one foot contacts theground and ends when that foot contacts theground again

Figure : Gait CycleFigure : Gait recognition systemProma Goswami , Roll No:97/IT/130005 20


Applications

Gait Biometric

making itwell-suited toidentifyingperpetrators at acrime scene fromCCTV footage

Military/intelligencesector

Identification ofshoplifters



Biometric system performance

Biometric systems are not perfect.There are two important types oferrors associated with biometricsystem

System decisions (i.e.accept/reject) is based onso-called thresholds

The False Acceptance is theprobability of wrongfully acceptingan impostor user

False Rejection is the probabilityof wrongfully rejecting a genuineuser.

Where FAR=FRR is known asEER at that threshold value

Figure : FAR& FRR as function ofthreshold



Advantage & Disadvantage of these Biometric methods

Finger print

AdvantageWell established forensictechnique, High accuracyModern fingerprintscanners→ Low Cost

DisadvantageFingerprints of smallfraction of population maynot be suitableLarge computation, Useracceptance ↓

Gait analysis

AdvantageIdentification in adverseconditions like smog,largedistanceAcceptable biometric likeface

DisadvantageMay not remain invariantin tiredness, age andhealthcan be obscured,wearingloose fitting clothes



Thank you

THANK YOUFOR YOUR ATTENTION


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