Biometrics Viktor MINKIN email@example.com Outline 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 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 Receiver operating characteristics (ROC) False Acceptance Rate 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 Finger source 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 Directional Directional image 1 image 2 irregularity Code Smoothing Binarization Skeleton Skeleton Minutiae formation cleaning search Fingerprints Comparative testing Fingerprints Fingerprint information Unimodal Systems Facial ID Illumination Head pose Occlusion Unimodal Systems Hand Vein Questionable accuracy Hand geometry Unimodal Systems Retinal Pattern Highest accuracy Even more intrusive than iris recognition Unimodal Systems Facial Thermo image and VibraImage Non-intrusive Lie detection View-dependent Emotion control Depends heavily on Criminals detector human factors, Medical monitoring body temperature 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 Classifier Identity Features Modality n-1 Features Modality n Multi-modal Systems 3 -Elsys includes BiCard, VibraImage, BioFinger 3D-Elsys is biological and behavioral identification system Multi-modal Systems The Earth population in 2010 will be about 10.000 M. people. The biometric document (ID card) market is more than 10.000 $ M. There are 3 different ID card technologies: 1. Card with additional memory (chip, CD,..) 2. Card with 2d-bar code 3. BiCard (3D-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/div893/biometrics/ Literature – http://www.itl.nist.gov/iaui/894.03/pubs.html#fing Patents - http://www.elsys.ru/patents.php History and Future 19 century- not automated identification 20 century- person identification 21 century- emotion detection Viktor Minkin Biometrics firstname.lastname@example.org Thank you!