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Biometric Technologies

Biometric Technologies. Team 3: Steven Golikov Barbara Edington Melanie Johnson Bashir Amhed Borming Chiang. Introduction to Biometrics. History of Biometrics Biometrics is the study of biological “data” Biometrics has a very long tradition

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Biometric Technologies

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  1. Biometric Technologies Team 3: Steven Golikov Barbara Edington Melanie Johnson Bashir Amhed Borming Chiang

  2. Introduction to Biometrics • History of Biometrics • Biometrics is the study of biological “data” • Biometrics has a very long tradition • The Egyptians used the length of a person’s forearm to determine their identification for wage payment • There are many different biometrics used for identification: • Fingerprints • Eye – Retinal or Iris • Facial Recognition • Voice • Signature • Dental • DNA • www.biometricscatalog.org

  3. Iris and Retinal Scanning • The Basics of Human Eye • Rationales • How do the Technologies Work? • Applications • Performance Metrics • Pros and Cons • Commercial Products

  4. The Basics of Human Eye • Iris – • The Shutter of our biological camera • The plainly visible colored ring underneath cornea. • Iris surrounds the pupil • A muscular structure which controls the amount of light entering into the eye, and it has very intricate details such as colors, striations, pits and furrows. • Retina – • The film of the camera • Located in the back of the eye where the Optic nerve connects. • The blood vessels pattern in the retina are unique to each individual. Source: http://www.stlukeseye.com/Anatomy.asp

  5. Rationales for Use Iris contains intricate details such as striations, pits and furrows. Two Iris’s are not alike. There is no detailed correlation between the patterns of identical twins or even between the left and right eye of the same individual. Expressed by the Individual’s Phenotype, Not Genotype Iris Collage Retinal Image - Twin 1 Retinal Image -Twin 2 The patterns of blood vessels in the retina is extremely unique to individuals. There is no detailed correlation between the patterns of identical twins or even between the left and right eye of the same individual. Source of images: http://www.cl.cam.ac.uk/users/jgd1000/

  6. How does Iris Recognition Works? • A picture of the eye is taken from within 1< meter distance and Iris portion is extracted • An Iris code of 512 Bytes is generated using functions called 2-D wavelets. This code is unique to one eye of one individual. • Iris code is then compared to other Iris codes that are stored in the database Source of images: http://www.iridiantech.com/

  7. History of Iris Recognition • 1936 – Idea proposed by ophthalmologist Frank Burch • 1949 - The idea documented in an ophthalmology textbook by James Doggarts • 1980's - The idea had appeared in James Bond films, but it still remained science fiction and conjecture. • 1987 - two ophthalmologists, Aran Safir and Leonard Flom, patented this idea • 1989 - John Daugman (then teaching at Harvard University) try to create actual algorithms for iris recognition • John Daugman algorithms patented in 1994, are the basis for all current iris recognition systems and products

  8. Commercial Applications -Iris • The major applications of this technology so far have been: • Aviation security and controlling access to restricted areas at airports • London Heathrow, Amsterdam Schiphol, Frankfurt, Athens, and several Canadian airports, Charlotte/Douglas International Airport in North Carolina • Database access and computer login • Access to buildings and homes • Hospital settings, including mother-infant pairing in maternity wards • Border control "watch list" database searching at border crossings • On the Pakistan Afghanistan border, the United Nations High Commission for Refugees uses these algorithms for anonymous identification of returning Afghan refugees receiving cash grants at voluntary repatriation centres • Other law enforcement agency programs such Jail Security Prisoner Identification – 1994 - Lancaster County Prison in Pennsylvania became the first correctional facility to employ the technology.

  9. Performance Comparison Source: AIM Japan, Automatic Identification Seminar, Sept.14, 2001

  10. Iris Scanning The uniqueness of Irises, even between the left and right eye of the same person, makes iris scanning very powerful for identification purposes. The likelihood of a false positive is extremely low and its relative speed and ease of use make it a great potential biometric. The only drawbacks are the potential difficulty in getting someone to hold their head in the right spot for the scan if they are not doing the scan willingly. Retina Scanning Retina scan devices are probably the most accurate biometric available today. The continuity of the retinal pattern throughout life and the difficulty in fooling such a device also make it a great long-term, high-security option. The high cost of the proprietary hardware as well as the inability to evolve easily with new technology make retinal scan devices a bad fit for most situations. It also has the stigma of consumer's thinking it is potentially harmful to the eye, and in general, not easy to use. Pros and Cons

  11. Commercial Products and Vendors

  12. Face Recognition • Background • Algorithms • FERET/FRVT • Research • Commercial Products

  13. In simplistic form, A signature is created from a sensor’s observation An algorithm normalizes the signature A matcher compares the normalized structure to the database. Face Recognition: The Basics

  14. The Algorithms • Eigenfaces • Standard Principle Components Analysis (PCA) • PCA & LDA • LDA: Linear Discriminant Analysis • Combination based on the University of Maryland algorithm tested in FERET. • Baysian • An Intrapersonal/Extrapersonal Image Distance Classifier based on the MIT algorithm tested in FERET. • Elastic Bunch Graphing • Based on the USC algorithm tested in FERET • Uses localized landmark features represented by Gabor jets

  15. Elastic Bunch Graphing

  16. FERET and FRVT • FERET • DARPA and Army Research Laboratory • 1994-1996 • A unified means of testing algorithms for easier comparison • FRVT • Designed by Govt and Law enforcement agencies • 2000 and 2002 • Tested ability to compare images to those stored in a database • Females and younger people were harder to recognize

  17. Current Research • 3-D morphable models • Not as affected by lighting and pose as is 2-D • MERL (Mitsubishi) and Ohio State U • Identical twin Israeli students • Created a 3-D scanner that uses light to scan the image • Algos measure the distances between points and compare to database images • Factors • False positives • Privacy issues • Environment – lighting, movement, etc

  18. Commercial Product • FaceIT (from Visionics / Identix) • $100 • Developed from an algorithm out of Rockefeller University • Viisage • From MIT algorithm based on eigenfaces • TrueFace (from Miros then acquired by Sol Universe) • FaceOK – (from Titanium Technology) • $89 • PC user security

  19. Introduction to Fingerprint Recognition • Fingerprint is the most referred biometric mechanism used today. • Fingerprint has the uniqueness feature – the studies shows that chance of same fingerprint between two individuals (even in twins) is one in one billion. • Fingerprint has been widely adopted (low cost) for authentication, identification and criminal investigation.

  20. Uniqueness of Fingerprint • Fingerprint is unique because of the two distinct feature • Persistence – the basic characteristic of fingerprint do not change in time. • Individuality – one over 1 billions !! • Fingerprints are comprised of various types of ridge patterns: left loop, right loop, arch, whorl and tented arch. • The discontinuities that interrupt these smooth ridge patterns are called Minutia. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern

  21. Fingerprint Capturing and Analysis • Fingerprint Matching • Minutae-based • Correlation based - requires precision location of registration point • Fingerprint Classification • The technique to assign a fingerprint into one of the several pre-specified types already established with indexing mechanism • Fingerprint Enhancement • It is essential to incorporate a fingerprint enhancement algorithm with respect to the quality of the fingerprint images in the minutiae extraction module in order to ensure the accuracy of automatic fingerprint identification/verification

  22. The Identification Workflow of Fingerprint Device

  23. Hardware - BioTouch® 200 USB Fingerprint Reader Software - BioLogon for Windows DemonstrationIdentix BioTouch® 200 USB Fingerprint Reader

  24. Dynamic Signature Verification • What is It? • Uses • Advantages • Disadvantages • Future

  25. What is It? • On-line vs Off-line signature Verification • Vision-Based, Non-Vision • Dynamic Signature – unique as DNA • Measures speed, pressure of the pen • Captures x, y, z location of the writing

  26. Uses • Point of Sale applications • Workflow automation • Security • Authentication – replaces password, PIN, keycards, identification card • Financial – account opening, withdrawal • Wireless device security

  27. Advantages • Signatures already accepted as a means of identification so people willing to accept electronic signature. • Changes in signing are consistent and have recognizable pattern. • Is not forgotten, lost, or stolen, so simple and natural way for enhanced computer security and document authorization. • unique to an individual and almost impossible to duplicate.

  28. Disadvantages • Secured authentication • Difficult to segment strokes as writing styles are varied and have no set standard • Electronic tablets or digitizers are bulky and complex.

  29. Future • Administrative Simplification (AS) of the Health Insurance Portability and Accountability Act (HIPAA) • IT expenditures • Frost & Sullivan - $5.7M in 2003 up to $123.3M by 2009 • Mobile phones, Internet, tablet PC’s • PC/Network Access, e-Commerce and telephony, physical access and surveillance businesses

  30. Thank You • Have any questions or comments? Team 3: Steven Golikov Barbara Edington Melanie Johnson Bashir Amhed Borming Chiang

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