An Introduction to Biometric Recognition - 國立臺灣 …cmlab.csie.ntu.edu.tw/~ipr/mmsec2008/data/lecture/Lecture12 - An...An Introduction to Biometric Recognition Anil K. Jain,

An Introduction to Biometric

RecognitionAnil K. Jain, Fellow, IEEE, Arun Ross, Member, IEEE, and Salil Prabhakar, Member, IEEE,

IEEE Transactions on Circuits and Systems for Video Technologies, vol. 14, no. 1, Jan. 2004

Multimedia Security

2

Outline (1/2)

• Part Ⅰ. Introduction

• Part Ⅱ. Biometric System

• Part Ⅲ. Biometric System Errors

• Part Ⅳ. Comparison of Various Biometrics

• Part Ⅴ. Application of Biometric Systems

3

Outline (2/2)

• Part Ⅵ. Advantage and Disadvantage of

Biometrics

• Part Ⅶ. Limitation of (Unimodal) Biometric

Systems

• Part Ⅷ. Multimodal Biometric Systems

• Part Ⅸ. Social Acceptance and Privacy

Issues

4

Ⅰ. Introduction (1/5)

• The term biometric comes from the Greek

words bios (life) and metrikos (measure).

• Biometrics – individuals’ physiological

and/or behavioral characteristics.

5


• Biometric Recognition

– “who she is” vs. “what she possesses”

6


• What biological measurements qualify to

be a biometric?

a) Universality

b) Distinctiveness

c) Permanence

d) Collectability

7


• In a practical biometric system, there are

a number of other issues that should be

considered…

a) Performance

b) Acceptability

c) Circumvention

8


In conclusion, the system should meet…

a) Accuracy

b) Speed

c) Resource requirement

d) Be harmless to the users

e) Be accepted by the intended population

f) Be sufficient robust to various attack

9

Ⅱ. Biometric System (1/10)

• A biometric system is essentially a pattern

recognition system.

10


• A biometric system is designed using the following four main modules.

1) Sensor module(encapsulating a quality checking module)

2) Feature module

3) Matcher module (encapsulating a decision making module)

4) System database module

11


A sample flow chart:

FeatureExtractor

Sensor

Qualifychecker

SystemDatabase

True / False

Matcher

DecisionMaker

template

The templates in the system databasemay be updated over time.

12


• A biometric system may operate either in

verification mode or identification mode.

a) Verification mode:“Does this biometric data belong to Bob? ”

b) Identification mode:“Whose biometric data is this? ”

13


SystemDatabase

LoginInterface

Get Name & Snapshot

QualityChecker

Check Quality

FeatureExtractor

Enrollment

Template

14


SystemDatabase

True / False

LoginInterface

Get Name & SnapshotOne template

FeatureExtractor

Extract Features

Matcher

One match

Verification

Claimed identity

15


SystemDatabase

User’s identity or“user unidentified”

LoginInterface

Get Name & SnapshotN templates

FeatureExtractor

Extract Features

Matcher

N match

Identification

16


• “Recognition” is the generic term of

verification and identification.

• We do not make a distinction between

verification and identification.

17


Describing the verification problem:

a) An input feature vector: XQ

b) A claimed identity: I

c) The biometric template corresponding to I : XI

d) The similarity between XQ and XI: S(XQ, XI)

e) The predefined threshold of similarity: t

f) True (a genuine user): ω1 ; False (an imposter): ω2

otherwise

),(S if

,2

,1),(

tXX

ω

ωXI

IQ

Q

18


The identification problem…a) The identity enrolled in the system: Ik, k=1, 2,…, N

b) The reject case: IN+1

c) The biometric template corresponding to Ik : XIk

otherwise

,,2,1,)},(Sax{ if

,

,

1

NktXXm

I

IX kIQ

N

k

Q

19

Ⅲ. Biometric System Errors (1/9)

• A biometric verification system makes two types of errors:

1) mistaking biometric measurements from two different persons to be from the same person (called false match)

2) mistaking two biometric measurements from the same person to be from two different persons (called false non-match)

20


Hypothesis testing:

1) H0: input XQ does not come from the

same person as the template XI

2) H1: input XQ comes from the same

person as the template XI

21


If S (XQ , XI) ≧ t , then decide D1 , else decide D0 .

Decision:

D0: person is not who she claims to be

D1: person is who she claims to be.

22


• Such a hypothesis testing formulation

contains two type of error:

• Type Ⅰ(α): false match (D1, when H0)

• Type Ⅱ(β): false non-match (D0, when H1)

FMR is the probability of Type I errorFNMR is the probability of Type II error

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DecisionThreshold (t )

Matching Score (s )

Pro

babili

ty (

p)

∞-∞

ImposterDistribution

p (s|H0)

GenuineDistribution

p (s|H1)

FNMR = P (D0|H1) FMR = P (D1|H0)

24


The errors in identification mode:

FMRN: the identification false match rate

FNMRN: the identification false non-match rate

• FMRN ＝ 1 – (1 – FMR)N ～ N × FMR

• FNMRN ～ FNMR

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• Some situation may lead to following

formulation of FMRN and FNMRN.

a) FNMRN ＝ RER + (1 - RER) × FNMRRER: retrieval error rate

b) FMRN ＝ 1 – (1 – FMR)N×P

P: the average percentage of database searched

during the identification

26


False Non-match Rate (FNMR)

Fals

e M

atc

h R

ate

(FM

R)

ForensicApplications

High-securityApplications

CivilianApplications

27


• Important specifications in a biometric

system:

1) FMR: false match rate

2) FNMR: false non-match rate

3) FTC: failure to capture (e.g., a faint fingerprint)

4) FTE: failure to enroll

28

Ⅳ. Comparison of Various

Biometrics (1/10)

• Each biometric has its strengths and

weaknesses.

• No biometric is “optimal”.

• A brief introduction of the commonly used

biometrics is given below…

29


Biometrics (2/10)

DNA

– 1-D ultimate unique code

– identical twins have identical DNA patterns

– contamination and sensitivity

– automatic real-time recognition issues

– privacy issues

Ears

– The shape of the ear

– the structure of the cartilaginous tissue of the pinna

30


Biometrics (3/10)

Face - Also used by humans

1) the location and shape of facial attributes

2) the overall analysis of the face image

Requiring a simple background and illumination

In practice, …

– Detect the face

– Locate the face

– Recognize the face

31


Biometrics (4/10)

Facial, hand, and hand vein infrared thermogram

– A thermogram-based system does not require

contact and is non-invasive

– Infrared sensors are prohibitively expensive

手掌靜脈辨識系統資料來源：FUJITSU, Taiwan

32


Biometrics (5/10)

Fingerprint

– A fingerprint scanner costs about US $20

– Single vs. Multiple

33


Biometrics (6/10)

Gait

Hand and finger

Geometry

34


Biometrics (7/10)

Iris– stabilize during the first two years of life

– the irises of identical twins are different

– extremely difficult to surgically tamper the texture of

the iris

35


Biometrics (8/10)

Keystroke

Odor

Palmprint

36


Biometrics (9/10)

Retinal scan

– the most secure biometric

– reveal some medical conditions

Signature

– professional forgers may be able to reproduce signatures that fool the system

Voice

– a combination of physiological and behavioral biometrics

37


Biometrics (10/10)

38

Ⅴ. Application of Biometric

Systems (1/3)

• The application of biometric can be

divided into three main groups:

1) CommercialATM, credit card, cellular phone, distance learning, etc.

2) GovernmentID card, driver’s license, social security, passport control, etc.

3) Forensicterrorist identification, missing children, etc.

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Systems (2/3)

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Systems (3/3)

250.9

399.5

523.9

729.1

1049.6

1440.6

1905.4

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1999 2000 2001 2002 2003 2004 2005

SOURCE: The `123' of Biometric Technology

REVEN

UE (

US$m

)

41

Ⅵ. Advantage and Disadvantage of

Biometrics (1/2)

Advantage

• All the users of the system have equal

security level.

• Between 20% and 50% of all help desk

calls are for password resets.

42

Ⅵ. Advantage and Disadvantage of

Biometrics (2/2)

Disadvantage

• Speed is perceived as the biggest problem.

• FMR will increase when scaling up an

identification application.

43

Ⅶ. Limitation of (Unimodal)

Biometric Systems (1/2)

1) Noise in sensed data

2) Intra-class variations

44

Ⅶ. Limitation of (Unimodal)

Biometric Systems (2/2)

3) Distinctivenesse.g. Hand geometry, face, etc.

4) Non-universality

5) Spoof attacks

45

Ⅷ. Multimodal Biometric Systems

(1/19)

Data Fusion Level of Fusion

a) Fusion at Sensor level

b) Fusion at Feature level

c) Fusion at Opinion level

d) Fusion at Decision level

46


(2/19)

decision

FeatureExtraction

Biometricsnapshot

MatchingDecisionMaking

FeatureExtraction

Biometricsnapshot

Fusion

SystemDatabase

features

features

47


(3/19)

• This combination strategy is usually done

by a concatenation of the feature vectors

extracted by each feature extractors.

• This yields an extended size vector set.

48


(4/19)

Two drawbacks:

1) There is little control over the contribution

of each vector component on the result.

2) Both feature extractors should provide

identical vector rates.

49


(5/19)

• Although it is a common belief that the

earlier the combination is done, the better

result is achieved, state-of-the-art data

fusion relies mainly on the opinion and

decision level.

50


(6/19)

decision

FeatureExtraction

Biometricsnapshot Matching

DecisionMaking

FeatureExtraction

Biometricsnapshot

FusionSystemDatabase

Matching

rank values

rank values

51


(7/19)

• The score must be adjusted first:

( Normalization must be done. )

– The similarity measures must be converted

into distance measures.

– The score generated by each classifier must

have same range. [ex. 0-100]

52


(8/19)

• The combination strategies can be

classified into three main groups:

– Fixed rules / equal weight

– Trained rules / unequal weight

– Adaptive rules / adaptive weight

53


(9/19)

• The most popular schemes are:

– Weight sum

– Weight product

– Decision trees ( base on if-then-else )

54


(10/19)

Classifier 1 Classifier 2 Classifier 3

Score1 > t1

Score2 > t2

Score3 > t3 False True

Yes

Yes Yes

No

No No

NoYes

Score2 > t2

False

False True

55


(11/19)

decision

FeatureExtraction

Biometricsnapshot Matching

FusionSystemDatabase

Matching

DecisionMaking

DecisionMaking

FeatureExtraction

Biometricsnapshot

decision

decision

56


(12/19)

• In this last case, the Borda count method

can be used for combining the classifiers’

outputs.

• This approach overcomes the scores

normalization that was mandatory for the

opinion fusion level.

57


(13/19)

Classifier 1

Classifier 2

Classifier 3

class 2

class 1

class 3

class 1

class 2

class 3

class 2

class 3

class 1

class 2=2

class 1=1

class 3=0

class 1=2

class 2=1

class 3=0

class 2=2

class 3=1

class 1=0

class 2=5

class 1=3

class 3=1

58


(14/19)

• One problem that appears with decision

level fusion is the possibility of ties.

• For verification applications, at least three

classifiers are needed.

59


(15/19)

• An important combination scheme at the

decision level is the serial and parallel

combination, also known as “AND” and

“OR” combination.

System 1 System 2

System 1

System 2

60


(16/19)

• The AND combination improves the False

Acceptance Ratio.

• The OR combination improves the False

Rejection Ratio.

61


(17/19)

MultimodalBiometrics

Multiplematchers

Multiplesnapshots

Multipleunits

Multiplebiometrics

Multiplesensors

right index &middle fingers

optical &capacitance

sensors

minutiae &non-minutiae

based matchers

face &fingerprint

two attempts ofright index finger

62


(18/19)

Example of Multimodal Biometric Systems

“Person Identification Using Multiple Cues” Face, Voice

“Expert Conciliation for Multimodal Person Authentication Systems using Bayesian Statistics” Face, Speech

“Integrating Faces and Fingerprints for Personal Identification” Face, fingerprint

“Personal Verification using Palmprint and Hand Geometry Biometric” Palmprint and Hand Geometry

“Bioid: A Multimodal Biometric Identification System” voice, lip motion, face

63


(19/19)

• A combination of uncorrelated modalities

is expected to result in a better

improvement in performance.

• A combination of uncorrelated modalities

can significantly reduce the FTE.

• However, the cost of the system may

increase and the system may cause

inconvenience.

64

Ⅸ. Social Acceptance and Privacy

Issues (1/3)

Social Acceptance

• The ease and comfort in interaction with a

biometric system contribute to its

acceptance.

• Biometric characteristics captured without

the knowledge of the user is perceived as

a threat to privacy by many individuals.

65


Issues (2/3)

Privacy Issues

• Biometrics can be used as one of the most

effective means for protecting individual

privacy.

• Biometric characteristics may provide

additional information about the

background of an individual.

66


Issues (3/3)

• Legislation is necessary to ensure that

such information remains private and that

its misuse is appropriately punished.

• Most of the commercial biometric systems

available today store a template in an

encrypted format.

Documents

An Introduction to Biometric Recognition - 國立臺灣 …cmlab.csie.ntu.edu.tw/~ipr/mmsec2008/data/lecture/Lecture12 - An...An Introduction to Biometric Recognition Anil K. Jain,