CUBS, University at Buffalo Biometrics CUBS, University at Buffalo http://www.cubs.buffalo.edu http://www.cedar.buffalo.edu/~govind/CSE717 govind@buffalo.edu

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 Conventional security systems rely on PINs, passwords and other token or key based methods for authentication and identification of users. Though these systems are easy to use, they are insecure as the tokens can be lost, stolen or used by more than one person. With each service requiring different form and means of identification, the multiplicity of authentication schemes becomes difficult to manage.  

Biometrics Definition Examples Physical Biometrics 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 Biometrics offers a promising solution for reliable and uniform identification and verification of an individual. Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits. Physical biometrics rely on physiological features such as fingerprints, hand geometry, iris pattern, facial features etc. for identity verification. Behavioral biometrics depends upon behavioral features such as speech patterns, handwriting, signature, walking gait etc. for authentication. These traits are unique to an individual and hence cannot be misused, lost or stolen. Biometrics are based on established scientific principles as a basis for authentication.

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 Sensor Feature Extraction Database Enrollment ID : 8809 Biometric Sensor Feature Extraction Matching Authentication 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

Applications And Scope of Biometrics Technologies Horizontal Applications Key Vertical Markets Fingerprint Civil ID Government Sector Facial Recognition Surveillance and Screening Travel and Transportation Iris Scan PC / Network Access Financial Sector Middleware Retail / ATM / Point of Sale Health Care AFIS Criminal ID Law Enforcement Voice Scan eCommerce / Telephony   Hand Geometry Physical Access / Time and Attendance Signature Verification Keystroke Dynamics

Biometric Modalities Common modalities Iris Fingerprint Face Voice Verification Hand Geometry Signature Other modalities Retinal Scan Odor Gait Keystroke dynamics Ear recognition Lip movement The corrugated surface of the fingerprint is made up of ridges and valleys cover that the entire palmer surface of the hand. The flow pattern of these ridges and valleys are unique to each individual. These patterns that are used for identification and authentication. The image below shows the image of a fingerprint along with the distinguishing features on the print.   The flow of the ridges and patterns has been classified into 5 broad classes. This classification is used to catalog the fingerprints and also in authenticating two prints. Henry systems follow an elaborate classification scheme of cataloging and filing forensic prints. Fig1 shows the different classes of ridge flows. This methods cannot be used to distinguish between two fingerprints. Fig 2 shows the distinguishing features on the fingerprint. These features are discontinuities or anomalies in the normal flow of ridges on the surface of the finger. These features are termed as minutiae (small details). There are eighteen different types of minutiae. Fig1 shows the most commonly encountered ones and their names. Fig 2 shows a thumbprint captured on paper. These are typically the kind of images that forensic AFIS (Automatic Fingerprint Identification Systems) have to deal with. The quality of the print not only deteriorates during capture but also during storage and hence AFIS systems are more sophisticated than their biometric counterparts.

Fingerprint Verification Fingerprints can be classified based on the ridge flow pattern The corrugated surface of the fingerprint is made up of ridges and valleys cover that the entire palmer surface of the hand. The flow pattern of these ridges and valleys are unique to each individual. These patterns that are used for identification and authentication. The image below shows the image of a fingerprint along with the distinguishing features on the print.   The flow of the ridges and patterns has been classified into 5 broad classes. This classification is used to catalog the fingerprints and also in authenticating two prints. Henry systems follow an elaborate classification scheme of cataloging and filing forensic prints. Fig1 shows the different classes of ridge flows. This methods cannot be used to distinguish between two fingerprints. Fig 2 shows the distinguishing features on the fingerprint. These features are discontinuities or anomalies in the normal flow of ridges on the surface of the finger. These features are termed as minutiae (small details). There are eighteen different types of minutiae. Fig1 shows the most commonly encountered ones and their names. Fig 2 shows a thumbprint captured on paper. These are typically the kind of images that forensic AFIS (Automatic Fingerprint Identification Systems) have to deal with. The quality of the print not only deteriorates during capture but also during storage and hence AFIS systems are more sophisticated than their biometric counterparts. Fingerprints can be distinguished based on the ridge characteristics

Feature Extraction X Y θ T 106 26 320 R 153 50 335 255 81 215 B

Matching T(ΔX, ΔY , Δθ)? X Y θ T X Y θ T Rotation Scaling Translation Elastic distortion X Y θ T 106 26 320 R 153 50 335 255 81 215 B X Y θ T 215 08 120 R 213 20 145 372 46 109 B T(ΔX, ΔY , Δθ)?

Face Recognition:Eigen faces approach Face detection and localization Eigen faces Normalization

Face Feature Representations Facial Parameters Eigen faces Semantic model

Speaker Identification Speaker Recognition Speaker Recognition Speaker Identification Speaker Verification Speaker Detection Text Dependent Independent Speech Codecs IVR Computer Access Transactions over phone Forensics Caller identification

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

Smoothened Signal Spectrum Vocal Tract modeling Speech signal Signal Spectrum Smoothened Signal Spectrum

Speaker Model F1 = [a1…a10,b1…b10] F2 = [a1…a10,b1…b10] ……………. FN = [a1…a10,b1…b10] …………….

Signature Verification Online Signature verification Off line Signature Verification

Matching – Similarity Measure Simple Regression Model Y = (y1 , y2 , …, yn) X = (x1 , x2 , …, xn) Similarity by R2 : 91% R2= Similarity by R2 : 31%

Dynamic Alignment 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.

Iris Recognition Sharbat Gula:The Afghan Girl Iriscode used to verify the match

Iris Recognition Iris Image Choosing the bits Gabor Kernel

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 False Accept Confusion matrix

Performance Curves: Score Distribution

Performance curves: FAR/FRR

Performance curves: ROC

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 <1% 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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