biometrics cubs, university at buffalo govind/cse717 [email protected]
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Biometrics
CUBS, University at Buffalohttp://www.cubs.buffalo.edu
http://www.cedar.buffalo.edu/~govind/[email protected]
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
80
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