an introduction to biometric recognition anil k. jain, fellow, ieee, arun ross, member, ieee, and...
TRANSCRIPT
An Introduction to Biometric Recognition
Anil 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
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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
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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
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Ⅰ. Introduction (1/5)
• The term biometric comes from the Greek words bios (life) and metrikos (measure).
• Biometrics – individuals’ physiological and/or behavioral characteristics.
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Ⅰ. Introduction (2/5)
• Biometric Recognition– “who she is” vs. “what she possesses”
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Ⅰ. Introduction (3/5)
• What biological measurements qualify to be a biometric?
a) Universality
b) Distinctiveness
c) Permanence
d) Collectability
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Ⅰ. Introduction (4/5)
• In a practical biometric system, there are a number of other issues that should be considered…
a) Performance
b) Acceptability
c) Circumvention
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Ⅰ. Introduction (5/5)
In conclusion, the system should meet…
a) Accuracyb) Speedc) Resource requirementd) Be harmless to the userse) Be accepted by the intended populationf) Be sufficient robust to various attack
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Ⅱ. Biometric System (1/10)
• A biometric system is essentially a pattern recognition system.
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Ⅱ. Biometric System (2/10)
• A biometric system is designed using the following four main modules.
1) Sensor module(encapsulating a quality checking module)
2) Feature module3) Matcher module
(encapsulating a decision making module)
4) System database module
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Ⅱ. Biometric System (3/10)
A sample flow chart:
FeatureExtractor
Sensor
Qualifychecker
SystemDatabase
True / False
Matcher
DecisionMaker
template
The templates in the system databasemay be updated over time.
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Ⅱ. Biometric System (4/10)
• 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? ”
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Ⅱ. Biometric System (5/10)
SystemDatabase
LoginInterface
Get Name & Snapshot
QualityChecker
Check Quality
FeatureExtractor
Enrollment
Template
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Ⅱ. Biometric System (6/10)
SystemDatabase
True / False
LoginInterface
Get Name & SnapshotOne template
FeatureExtractor
Extract Features
Matcher
One match
Verification
Claimed identity
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Ⅱ. Biometric System (7/10)
SystemDatabase
User’s identity or“user unidentified”
LoginInterface
Get Name & SnapshotN templates
FeatureExtractor
Extract Features
Matcher
N match
Identification
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Ⅱ. Biometric System (8/10)
• “Recognition” is the generic term of verification and identification.
• We do not make a distinction between verification and identification.
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Ⅱ. Biometric System (9/10)
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 IQQ
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Ⅱ. Biometric System (10/10)
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
kQ
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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)
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Ⅲ. Biometric System Errors (2/9)
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
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Ⅲ. Biometric System Errors (3/9)
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.
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Ⅲ. Biometric System Errors (4/9)
• Such a hypothesis testing formulation contains two type of error:
• Type (α):Ⅰ 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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Ⅲ. Biometric System Errors (5/9)
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)
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Ⅲ. Biometric System Errors (6/9)
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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Ⅲ. Biometric System Errors (7/9)
• 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
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Ⅲ. Biometric System Errors (8/9)
False Non-match Rate (FNMR)
Fals
e M
atc
h R
ate
(FM
R)
ForensicApplications
High-securityApplications
CivilianApplications
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Ⅲ. Biometric System Errors (9/9)
• Important specifications in a biometric system:
1) FMR: false match rate
2) FNMR: false non-match rate3) FTC: failure to capture (e.g., a faint fingerprint)
4) FTE: failure to enroll
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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…
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Ⅳ. Comparison of Various 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
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Ⅳ. Comparison of Various Biometrics (3/10)
Face - Also used by humans1) 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
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Ⅳ. Comparison of Various 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
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Ⅳ. Comparison of Various Biometrics (5/10)
Fingerprint– A fingerprint scanner costs about US $20– Single vs. Multiple
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Ⅳ. Comparison of Various Biometrics (6/10)
Gait
Hand and finger
Geometry
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Ⅳ. Comparison of Various 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
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Ⅳ. Comparison of Various Biometrics (8/10)
Keystroke
Odor
Palmprint
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Ⅳ. Comparison of Various 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
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Ⅳ. Comparison of Various Biometrics (10/10)
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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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Ⅴ. Application of Biometric Systems (2/3)
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Ⅴ. Application of Biometric Systems (3/3)
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REV
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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.
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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.
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Ⅶ. Limitation of (Unimodal) Biometric Systems (1/2)
1) Noise in sensed data
2) Intra-class variations
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Ⅶ. Limitation of (Unimodal) Biometric Systems (2/2)
3) Distinctivenesse.g. Hand geometry, face, etc.
4) Non-universality
5) Spoof attacks
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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
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Ⅷ. Multimodal Biometric Systems (2/19)
decision
FeatureExtraction
Biometricsnapshot
MatchingDecisionMaking
FeatureExtraction
Biometricsnapshot
Fusion
SystemDatabase
features
features
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Ⅷ. Multimodal Biometric Systems (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.
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Ⅷ. Multimodal Biometric Systems (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.
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Ⅷ. Multimodal Biometric Systems (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.
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Ⅷ. Multimodal Biometric Systems (6/19)
decision
FeatureExtraction
Biometricsnapshot Matching
DecisionMaking
FeatureExtraction
Biometricsnapshot
FusionSystemDatabase
Matching
rank values
rank values
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Ⅷ. Multimodal Biometric Systems (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]
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Ⅷ. Multimodal Biometric Systems (8/19)
• The combination strategies can be classified into three main groups:
– Fixed rules / equal weight– Trained rules / unequal weight– Adaptive rules / adaptive weight
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Ⅷ. Multimodal Biometric Systems (9/19)
• The most popular schemes are:
– Weight sum– Weight product– Decision trees ( base on if-then-else )
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Ⅷ. Multimodal Biometric Systems (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
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Ⅷ. Multimodal Biometric Systems (11/19)
decision
FeatureExtraction
Biometricsnapshot Matching
FusionSystemDatabase
Matching
DecisionMaking
DecisionMaking
FeatureExtraction
Biometricsnapshot
decision
decision
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Ⅷ. Multimodal Biometric Systems (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.
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Ⅷ. Multimodal Biometric Systems (13/19)
Classifier 1
Classifier 2
Classifier 3
class 2class 1class 3
class 1class 2class 3
class 2class 3class 1
class 2=2class 1=1class 3=0
class 1=2class 2=1class 3=0
class 2=2class 3=1class 1=0
class 2=5class 1=3class 3=1
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Ⅷ. Multimodal Biometric Systems (14/19)
• One problem that appears with decision level fusion is the possibility of ties.
• For verification applications, at least three classifiers are needed.
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Ⅷ. Multimodal Biometric Systems (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
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Ⅷ. Multimodal Biometric Systems (16/19)
• The AND combination improves the False Acceptance Ratio.
• The OR combination improves the False Rejection Ratio.
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Ⅷ. Multimodal Biometric Systems (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
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Ⅷ. Multimodal Biometric Systems (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 Identific
ation” Face, fingerprint “Personal Verification using Palmprint and Hand Geomet
ry Biometric” Palmprint and Hand Geometry “Bioid: A Multimodal Biometric Identification System” voic
e, lip motion, face
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Ⅷ. Multimodal Biometric Systems (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.
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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.
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Ⅸ. Social Acceptance and Privacy 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.
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Ⅸ. Social Acceptance and Privacy 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.