Biometrics

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Biometrics

• CUBS, University at Buffalo
• http//www.cubs.buffalo.edu
• http//www.cedar.buffalo.edu/govind/CSE717
• govind_at_buffalo.edu

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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

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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

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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

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General Biometric System
Biometric Sensor
Feature Extraction
Database
Enrollment
Feature Extraction
Biometric Sensor
Matching
Authentication
Result
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Types of Authentication

• Verification
• Answers the question Am I whom I claim to be?
• Identity of the user is known
• 11 matching
• Identification
• Answers the question Who am I?
• Identity of the user is not known
• 1N 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

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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

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Applications And Scope of Biometrics
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Biometric Modalities

• Common modalities
• Iris
• Fingerprint
• Face
• Voice Verification
• Hand Geometry
• Signature
• Other modalities
• Retinal Scan
• Odor
• Gait
• Keystroke dynamics
• Ear recognition
• Lip movement

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Fingerprint Verification
Fingerprints can be classified based on the ridge
flow pattern
Fingerprints can be distinguished based on the
ridge characteristics
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Feature Extraction
X Y ? T
106 26 320 R
153 50 335 R
255 81 215 B
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Matching

• Rotation
• Scaling
• Translation
• Elastic distortion

X Y ? T
106 26 320 R
153 50 335 R
255 81 215 B
X Y ? T
215 08 120 R
213 20 145 R
372 46 109 B
T(?X, ?Y , ??)?
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Face RecognitionEigen faces approach
Face detection and localization
Eigen faces
Normalization
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Face Feature Representations

Facial Parameters

Semantic model
Eigen faces
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Speaker Recognition

• Speech Codecs
• IVR

• Computer Access
• Transactions over phone

• Forensics
• Caller identification

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Cepstral feature approach

• Preprocessing
• Feature Extraction
• Speaker model
• Matching

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Vocal Tract modeling
Signal Spectrum
Smoothened Signal Spectrum
Speech signal
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Speaker Model
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Signature Verification
Online Signature verification
Off line Signature Verification
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Matching Similarity Measure

• Simple Regression Model

Y (y1 , y2 , , yn) X (x1 , x2 , , xn)
Similarity by R2 91
Similarity by R2 31
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Dynamic Alignment
( y2 is matched x2, x3, so we extend it to be two
points in Y sequence.)
Dynamic alignment

• Similarity R2

Where (x1i, y1i, v1i) are points in the
sequence And a, b, c are the weights, e.g., 0.5,
0.5, 0.25

• 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.

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Iris Recognition
Sharbat GulaThe Afghan Girl
Iriscode used to verify the match
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Iris Recognition
Iris Image
Choosing the bits
Gabor Kernel
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Collage
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Hand Geometry
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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

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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
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Performance Curves Score Distribution
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Performance curves FAR/FRR
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Performance curves ROC
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State of the art
Biometrics State of the art Research Problems
Fingerprint 0.15 FRR at 1 FAR (FVC 2002) Fingerprint Enhancement Partial fingerprint matching
Face Recognition 10 FRR at 1 FAR (FRVT 2002) Improving accuracy Face alignment variation Handling lighting variations
Hand Geometry 4 FRR at 0 FAR (Transport Security Administration Tests) Developing reliable models Identification problem
Signature Verification 1.5(IBM Israel) Developing offline verification systems Handling skillful forgeries
Voice Verification lt1 FRR (Current Research) Handling channel normalization User habituation Text and language independence
Chemical Biometrics No open testing done yet Development of sensors Materials research
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Thank You

• ssc5_at_cedar.buffalo.edu