Biometrics Modalities Introduction (2)govind/CSE666/fall2007/Biometrics_Lectu… · Identification Problem Let FAR (=0.0001%) and FRR be for 1:1 case Then, False accepts = 1 in 10

Biometrics ModalitiesIntroduction (2)

Why Fingerprints?

§ Established Science ?§ Used in forensics to establish individual identity for over a century§ Scotland Yard 1900; FBI 1924

§ High Universality § Every person possesses the biometric

§ High Distinctiveness § Identical twins have different fingerprints but same DNA

§ High Permanence§ Formed in fetal stage and remain structurally unchanged

§ High Acceptability§ Fingerprint acquisition is non intrusive. Requires no training.

Henry Classification

Empirical Evidence

Challenges§ Robust Partial Fingerprint Matching § Security strength§ Accurate matching with subsets of features§ Speed efficiency

§ Provision for Changing Passwords § Cancelable Templates§ Security

§ Identification Mode§ Indexing and Searching Large Databases§ Identification Models

§ Resist Spoof Attacks

Minutiae Matching – Correspondence Problem

•Must NOT rely on singular points•Invariant to translation and rotation•Tolerate elastic spatial distortions•High speed and accuracy

R

Q

•Let f is the width of a ridge and valley•Nr and Nq the number of features on reference R and query Q templates•d is the number of allowable orientations

Security ComparisonTraditional Passwords§ Alpha-numeral characters (72 symbols ) can be represented by 7 bits§ Password of length 8 has approximately (128 8) = 256 possible

combinations§ Probability of a random match is 2-56 = 2 – bit strength

§ Bit strength = 56§ Password of length 6 has bit strength of 42

Fingerprints§ What is the minimum sensing area needed to achieve bit strength

of 45, given d=8, and the distance between the ridges is 15 pixels (say)?

0.32”x0.53” (d=8)

§ Choose d for a given scanning area and desired bit strength

§ Need additional information associated with features for higher security strength if the scanning area remains small

Challenges§ Robust Partial Fingerprint Matching § Security strength§ Accurate matching with subsets of features§ Speed efficiency



§ Resisting Spoof Attacks

Verification in clustersAnalysts’ Examination Method

Coupled Breadth-First Matching

B

Cb

c

A

a0 12

345

67

89

1011

12

02

3

1

4

678

9

1011

12

II RR

B

Cb

c

Aa0 1

2

345

67

8 910

11

12

02

3

1

4

67

89 10

11

12

II RR

A a A0124 a0124

Coupled Breadth-First Search Matching (2)

B

Cb

c

Aa0 1

2

345

67

8 910

11

12

02

3

1

4

67

89 10

11

12

II RR

B

Cb

c

Aa0 1

2

345

67

8 910

11

12

02

3

1

4

67

89 10

11

12

II RR

A,0,1,2,4,B A,0,1,2,4,b A,0,1,2,4,B,7,8,6,12,11,9,10 a,0,1,2,4,b,7,8,6,12,11,9,10

System Errors

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

No error

Genuine (w1)

False RejectGenuine

No errorImpostor

Impostor(w2)

Confusion matrix

15.88

1.96

1.58

Min.TER

N/A

2.49

3.32

Min.TER

N/A

N/A

1.88

Min.TER

19.96

5.46

6.55

Min.TER

9.28N/AN/A11.11FVC2002 DB3

1.161.57N/A3.37FVC2002 DB2

1.062.131.014.67FVC2002 DB1

EEREEREEREERDatabases

K-plets matchGenuine test:

138msImposter test:

136ms

Triplet matchGenuine test:

179msImposter test:

7ms

Brute-ForceBozorth3

Experimental Results

Super Bowl (2001) fans never knew it, but police video cameras

focused on their faces, one by one, as they streamed through

the turnstiles in Tampa on Sunday. Cables instantly carried the images to computers, which

spent less than a second comparing them with thousands

of digital portraits of known criminals and suspected

terrorists.In a control booth deep inside the

stadium, police watched and waited for a match

Surveillance and Watch Lists

Face Recognition: Eigen faces approach

Eigen faces Normalization

Face detection and localization

Face Feature Representations

Facial Parameters

Semantic modelEigen faces

Semantic Face Retrieval System

Query = []

[Spectacles = Yes]

[Spectacles = Yes Mustache = Yes]

[Spectacles = Yes Mustache = Yes Nose = Big]

Probabilities of Faces

Speaker RecognitionSpeaker Recognition

Speaker Identification Speaker VerificationSpeaker Detection

TextDependent

TextIndependent

TextDependent

TextIndependent

• Forensics

• Caller identification

• Speech Codecs

• IVR

• Computer Access

• Transactions over phone

Speech production

Speech production mechanism Speech production model

Impulse Train

Generator

Glottal Pulse ModelG(z)

Vocal TractModelV(z)

RadiationModelR(z)

Noise source

Pitch Av

AN

17 cm

Vocal Tract Modeling

Signal Spectrum Smoothened Signal Spectrum Speech signal

Iris Individuality

Iris Recognition

Choosing the bits

Gabor Kernel

Iris Image

Hand Geometry

Signature Verification

Off line Signature Verification

Online Signature verification

§ Simple Regression Model

-2000

-1500

-1000

-500

0

500

1000

1500

2000

0 50 100 150 200 250 300 350 400-2000

-1000

0

1000

2000

3000

4000

0 50 100 150 200 250 300 350 400-2000

-1000

0

1000

2000

3000

4000

-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

-500

0

500

10001500

2000

25003000

35004000

0 50 100 150 200 250 300 350 400-500

0

500

1000

1500

2000

2500

3000

3500

0 50 100 150 200 250 300 350 400-500

0500

10001500

2000

2500

3000

35004000

-500 0 500 1000 1500 2000 2500 3000 3500

Similarity by R2 : 31%

],...,,[ 321 nxxxxX =

•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 =

( 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

Challengesü Robust Matching § Partial Fingerprints from Commercial Sensors§ Resist Brute Force attacks§ Deal with subsets of features§ Speed Efficiency




Cancelable Challenges

Hashed values 1

Hashed values 2

Same?

• Images include different scanned area• Unordered set of features• Features different for two prints of the

same finger.• Similar fingerprints should have similar

hash values• Hash values should be invariant to

rotation/translation• Collision free• Cancelable

Fingerprint space Hash space

hf1

f2

h(f1)h(f2)

Image Quality and Registration

Dry printWet Print Creases

Intra ClassDifferent Sensors

Different Instances

xox x

xo

xo

x

xx o

x

x

oxx

x

ox

x

(a) (b)

(c)

Symmetric Hash Functions

),,,( 21 ni ccch ′′′ K

),,,( 21 ni ccch ′′′ K

ntcccrhntcccrtrctrctrc

cccccch

nn

n

nn

+=++++=++++++=

′++′+′=′′′

),,,()()()()(

),,,(

21121

21

21211

KK

K

KK

2211212

2

221

222

21

2

222

21

222

21212

),,,(2),,,(

)(2)(

)()()(

),,,(

ntcccrthccchr

ntcccrtcccr

trctrctrc

cccccch

nn

nn

n

nn

++=

++++++++=

++++++=

′++′+′=′′′

KK

KK

K

KK

trzzf +=)(


ü Provision for Changing Passwords § Cancelable Templates§ Security

ü Resist Spoof Attacks


Large Scale ChallengesSenator Edward M. Kennedy, Democrat of Mass., discussed the problems

faced by ordinary citizens mistakenly placed on terrorist watch lists. Between March 1 and April 6, airline agents tried to block Mr. Kennedy

from boarding airplanes on five occasions because his name resembled an alias used by a suspected terrorist who had been barred from flying on

airlines in the United States.

RACHEL L. SWARNS,NY Times, Aug 20, 2004

There are ~500 million border crossings/year (each way) in the U.S.

Large Scale Biometric Databases§ Biometrics are being deployed by US- VISIT) § Millions of records§ Largest study by NIST with 620,000 records § Speed and efficiency also become important§ 1:N matching needed for ‘screening’

0 2 4 6 8 10

x 105

0

2

4

6

8

10x 107

N

Fal

se A

ccep

ts

1.0 0.75

0.5

0.3

0.1

FARNFARNFAR NN ×≈−−== 2))1(1(N accepts False

Identification ProblemLet FAR (=0.0001%) and FRR be for 1:1 caseThen, False accepts = 1 in 10 for N=100,000

To reduce identification errors•Reduce FAR (Limited by technology)•Reduce N (number of records)•Use Multi-modal Approach•Develop a New Identification Model

There are ~500 million border crossings/year (each way) in the U.S.

Multi-Modal Binning

0

510

15

202530

3540

45

5 6 7 8 9 10 12 14 16 18 20 25 30

Number of Bins

Pen

etra

tion

Rat

eSignature

Hand Geometry

Combination

oo

o

oo

o

o

o xMost probable bin

Range-searched bins

Indexing

§ Hashes d-dim data into a single value§ B+ Tree used to index 1-dim data§ For d-dim data: 2 x d pyramids

[Berchtold, Böhm, Kriegel 1998]§ Pyramid value = Pyramid No (i) + Height (h)

35 – Dimensional Hand Geometry dataBest Penetration: 27%450 Training Set & 450 Testing SetFRR = 0

Indexing: tree-like organization of pointers to data

Searched nodes

Indexing Fingerprint Templates

rT

F

B A

C D

1 2

3

1A B C D

2

A B C D

3

A B C D

Fingerprint i,Minutia j

Fingerprint i,Minutia k

. . .

New Identification Model

0 0.1 0.2 0.3 0.4 0.5 0 . 6 0.7 0.8 0.9 10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1-FRR

FAR

Using only s1for thresholding

Using logistic regression of s1 and s

2Decision Function for Acceptance?

Step 1: S(Fred) = f(s1(Fred), s2(Fred))

Top-Match = arg max S(P)

P € {Fred, Alice, Bob}

Step 2: S(Top-Match) ~ ?

D(S(Top-Match), S(Second-Match), …) ~ ?

?

Biometric Templates

FingerprintMatcher

SignatureMatcher

s1 (Fred), s1(Alice), s1(Bob) s2(Fred), s 2(A

lice), s 2(Bob)

HandGeometryMatcher

FaceMatcher

SpeakerMatcher

GaitMatcher

PulseMatcher ………


ü Provision for Changing Passwords § Cancelable Templates§ Security



Spoof Attack

Gummy Finger

Liveness Detection

Skin Spectroscopy

Documents

Biometrics Modalities Introduction (2)govind/CSE666/fall2007/Biometrics_Lectu… · Identification Problem Let FAR (=0.0001%) and FRR be for 1:1 case Then, False accepts = 1 in 10