Biometrics

CUBS, University at Buffalohttp://www.cubs.buffalo.edu

http://www.cedar.buffalo.edu/~govind/CSE717govind@buffalo.edu

Conventional Security Measures

Possession or Token Based Passport, IDs, Keys License ,Smart cards,Swipe cards, Credit Cards

Knowledge Based Username/password PIN

Combination(P,K) ATM

Disadvantages of Conventional Measures Do not authenticate the user Tokens can be lost or misused Passwords can be forgotten Multiple tokens and passwords difficult to manage Repudiation

Biometrics

Definition Biometrics is the science of verifying and establishing the identity

of an individual through physiological features or behavioral traits Examples

Physical Biometrics Fingerprint, Hand Geometry,Iris,Face Measurement Biometric Dependent on environment/interaction

Behavioral Biometrics Handwriting, Signature, Speech, Gait Performance/Temporal biometric Dependent on state of mind

Chemical Biometrics DNA, blood-glucose

Requirements of Biometrics Universality

Each person should have the biometric Uniqueness

Any two persons should have distinctive characteristics Permanence

Characteristic should be invariant over time Collectability

Characteristic should be easy to acquire Acceptability

Is non-intrusive Non repudiation

User cannot deny having accessed the system

General Biometric System

Database

BiometricSensor

Feature Extraction

BiometricSensor

Feature Extraction

Matching

ID : 8809

Authentication

Enrollment

Result

Types of Authentication Verification

Answers the question “Am I whom I claim to be?” Identity of the user is known 1:1 matching

Identification Answers the question “Who am I?” Identity of the user is not known 1:N matching

Positive Recognition Determines if an individual is in the database Prevents multiple users from assuming same identity

Negative Recognition Determines if an individual is NOT in the given database Prevents single user from assuming multiple indentities

Aspects of a Biometric Systems

Sensor and devices Types of sensors Electrical and mechanical design

Feature representation and matching Enhancement, preprocessing Developing invariant representations Developing matching algorithms

Evaluation Testing

System Issues Large Scale databases Securing Biometric Systems Ethical, Legal and Privacy Issues

Biometric Modalities

Common modalities Iris Fingerprint Face Voice Verification Hand Geometry Signature

Other modalities Retinal Scan Odor Gait Keystroke dynamics Ear recognition Lip movement

Fingerprint Verification

Fingerprints can be classified based on the ridge flow pattern

Fingerprints can be distinguished based on the ridge characteristics

Feature Extraction

X Y θ T106 26 320 R

153 50 335 R

255 81 215 B

Matching

X Y θ T106 26 320 R

153 50 335 R

255 81 215 B

X Y θ T215 08 120 R

213 20 145 R

372 46 109 B

T(ΔX, ΔY , Δθ)?

•Rotation

•Scaling

•Translation

•Elastic distortion

Face Recognition:Eigen faces approach

Eigen faces Normalization

Face detection and localization

Face Feature Representations

Facial Parameters

Semantic modelEigen faces

Speaker RecognitionSpeaker Recognition

Speaker Identification Speaker VerificationSpeaker Detection

TextDependent

TextIndependent

TextDependent

TextIndependent

• Forensics

• Caller identification

• Speech Codecs

• IVR

• Computer Access

• Transactions over phone

Cepstral feature approach

Silence Removal

Cepstrum Coefficients

Cepstral Normalization Long time average

Polynomial Function Expansion

Dynamic Time Warping

Distance Computation

Reference Template

Preprocessing

Feature Extraction

Speaker model

Matching

Vocal Tract modeling

Signal Spectrum Smoothened Signal Spectrum Speech signal

Speaker Model

F1 = [a1…a10,b1…b10]

F2 = [a1…a10,b1…b10]

FN = [a1…a10,b1…b10]

…………….

…………….

9

1

21

9

11

1 5

jj

jjj

j

P

Pc

b

jP

Signature Verification

Off line Signature Verification

Online Signature verification

Simple Regression Model

-2 0 0 0

-1 5 0 0

-1 0 0 0

-5 0 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0-2 0 0 0

-1 0 0 0

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0-2 0 0 0

-1 0 0 0

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

-2000 -1500 -1000 -500 0 500 1000 1500 2000

Similarity by R2 : 91%

2

1 1

2

2

1

)()(

]))(([

n

i

n

iii

n

iii

yyxx

yyxxR2=

Y = (y1 , y2 , …, yn)

X = (x1 , x2 , …, xn)

Matching – Similarity Measure

-5 0 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0-5 0 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0-5 0 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

-500 0 500 1000 1500 2000 2500 3000 3500

Similarity by R2 : 31%

•DTW warping path in a n-by-m matrix is the path which has min cumulative cost. •The unmarked area is the constrain that path is allowed to go.

],...,,[ 221 myyyyY

],...,,[ 321 nxxxxX

( y2 is matched x2, x3, so we extend it to be two points in Y sequence.)

Similarity = R2

Dynamic Alignment

221

221

221 )()()(),( jijiji vvcyybxxajiCost

Where (x1i, y1i, v1i) are points in the sequence

And a, b, c are the weights, e.g., 0.5, 0.5, 0.25

S1

S2

Dynamic alignment

Iris Recognition

Sharbat Gula:The Afghan Girl Iriscode used to verify the match

Iris Recognition

Choosing the bits

Gabor Kernel

Iris Image

Collage

Hand Geometry

Evaluation of Biometric Systems

Technology Evaluation Compare competing algorithms All algorithms evaluated on a single database Repeatable FVC2002, FRVT2002, SVC2004 etc.

Scenario Evaluation Overall performance Each system has its own device but same subjects Models real world environment

Operational Evaluation Not easily repeatable Each system is tested against its own population

System Errors

FAR/FMR(False Acceptance Ratio) FRR/FNMR(False Reject Ratio) FTE(Failure to Enroll) FTA(Failure to Authenticate)

Genuine (w1)

Impostor(w2)

Genuine No error False Reject

Impostor False Accept

No error

Confusion matrix

Performance Curves: Score Distribution

Score Distribution(DB2)

-20

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2

Threshold

Pe

rce

nta

ge

Impostor

Genuine

Performance curves: FAR/FRR

FAR FRR Values(DB2)

-20

0

20

40

60

80

100

120

0 0.2 0.4 0.6 0.8 1 1.2

Threshold

Pe

rce

nta

ge

Series1

Series2

Performance curves: ROCROC curve(DB2)

-20

0

20

40

60

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100

120

-20 0 20 40 60 80 100 120

False positive

Tru

e p

os

itiv

e

State of the art

Biometrics State of the art Research Problems

Fingerprint 0.15% FRR at 1% FAR(FVC 2002)

Fingerprint EnhancementPartial fingerprint matching

Face Recognition

10% FRR at 1% FAR(FRVT 2002)

Improving accuracyFace alignment variationHandling lighting variations

Hand Geometry 4% FRR at 0% FAR(Transport Security Administration Tests)

Developing reliable modelsIdentification problem

Signature Verification

1.5%(IBM Israel) Developing offline verification systemsHandling skillful forgeries

Voice Verification

<1% FRR (Current Research)

Handling channel normalizationUser habituationText and language independence

Chemical Biometrics

No open testing done yet

Development of sensorsMaterials research

Thank You

ssc5@cedar.buffalo.edu