Biometrics Viktor MINKIN minkin elsys ru Outline Introduction

Biometrics Viktor MINKIN minkin@elsys. ru

Outline Introduction Biometric systems Biometric characteristics Fingerprints Unimodal systems Multi-modal systems Problems Links History and future

Introduction Biometrics [harmonized] Automated recognition of persons based on their biological or/and behavioral characteristics. Automated measurement of biological or/and behavioral characteristics of person for medical, security or psychological purposes.

Introduction Terms and definitions Template Capture Comparison Database Enrollment Matching Token User

Introduction Identification of a person – Verification/Verify • Comparing one to one • “Am I who I claim I am” – Identification • Comparing one to many • “Who am I”

Introduction Application • • Passport control Access to secured areas Surveillance ATMs Computer logins E-commerce Medicine Psychology

Introduction Traditional means of automatic identification (before biometrics) – Knowledge-based • Use “something that you know” • Examples: password, PIN – Token-based • Use “something that you have” • Examples: credit card, smart card, keys

Introduction Problems with traditional approaches – Token may be lost, stolen or forgotten – PIN may be forgotten or guessed by the imposters • (25% of people seem to write their PIN on their ATM card) Estimates of annual identity fraud damages per year: – – $1 billion in welfare disbursements $1 billion in credit card transactions $1 billion in fraudulent cellular phone use $3 billion in ATM withdrawals

Introduction The traditional approaches are unable to differentiate between an authorized person and an imposter a. Use biometrics which relies on “who you are” or “what you do”

Biometric Systems Requirements for an ideal biometric – Universality • Each person should have the characteristic – Uniqueness • No two persons should be the same in terms of the characteristic – Permanence • The characteristic should not change

Biometric Systems Issues in a real biometric system – Performance • Identification accuracy, speed, robustness, resource requirements – Acceptability • Extend to which people are willing to accept a particular biometric identifier – Faked protection • How easy is it to fool the system by fraudulent methods

Biometric Systems Identification accuracy • • • FAR = false acceptance rate FRR = false rejection rate EER = equal error rate TER = total error rate = FAR + FRR FER= false enrollment rate

Biometric Systems False Acceptance Rate Receiver operating characteristics (ROC) Equal Error Rate False Rejection Rate

Biometric Systems FAR/FRR and comparison threshold

Biometric Characteristics Static (biological) parameters Fingerprints Face Iris Hand geometry / vein Retinal pattern Facial thermogram Lip information DNA

Biometric Characteristics Dynamic (behavior) biometric parameters Signature Voice Motion Pulse

Biometric Characteristics Market Shares

Biometric Characteristics Market development

Fingerprints Accurate Comparatively cheap hardware Questionable acceptance

Fingerprints Optical technology Light source Finger Prism Lens Video Camera (CCD) Light reflects from the surface of the prism where the finger is not in contact with it, while it penetrates the surface of the prism where the finger touches the surface of the prism. The resulting image goes through a lens into a video camera.

Fingerprints Capacity technology

Fingerprints Fiber optic technology

Fingerprints Fingerprint types Arches Loops Whorl Minutia types Bridge Dot Ridge Ending Bifurcation Enclosure

Fingerprints Core & Deltas

Fingerprints Fingerprint minutiae

Fingerprints Image transformation Source FFT Flow field Directional image 1 Directional image 2 Directional irregularity Code Smoothing Binarization Skeleton formation Skeleton cleaning Minutiae search

Fingerprints Comparative testing

Fingerprints Fingerprint information

Unimodal Systems Facial ID Illumination Head pose Occlusion

Unimodal Systems Hand Vein Hand geometry Questionable accuracy

Unimodal Systems Retinal Pattern Highest accuracy Even more intrusive than iris recognition

Unimodal Systems Facial Thermo image and Vibra. Image Non-intrusive View-dependent Depends heavily on human factors, body temperature Lie detection Emotion control Criminals detector Medical monitoring Psychology testing

Multi-modal Systems Why multimodal [multiple] person identification? – Quest for non-intrusive identification methods • No special purpose hardware needed • Works potentially at greater distances – “Traditional” arguments for going multimodal: • Increasing performance • Increasing robustness – Mono-modal recognition techniques are likely to reach in a close future a saturation in performance.

Multi-modal Systems: Fusion “Early integration” or “sensor fusion” Integration is performed on the feature level Classification is done on the combined feature vector Features Modality 1 Features Modality 2 Features Modality n-1 Features Modality n Classifier Identity

Multi-modal Systems 3 -Elsys includes Bi. Card, Vibra. Image, Bio. Finger 3 D-Elsys is biological and behavioral identification system

Multi-modal Systems The World population in 2000 was about 6. 000 M. people. The biometric document (ID card) market is more than $6. 000 There are 3 different ID card technologies: 1. Card with additional memory (chip, CD, . . ) 2. Card with 2 d-bar code 3. Bi. Card (3 D-Elsys)

Problems Errors rate Misunderstanding of real advantages and problems Incomplete true about biometric systems

Links International Biometric Group - http: //www. biometricgroup. com NIST - http: //www. itl. nist. gov/div 893/biometrics/ Literature – http: //www. itl. nist. gov/iaui/894. 03/pubs. html#fing Patents - http: //www. elsys. ru/patents. php

Biometrics evolution 19 century- not automated identification 20 century- biometric identification 21 century- emotion recognition and detection

Viktor Minkin Biometrics minkin@elsys. ru Thank you! 2004