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Biometrics II

Biometrics II. Performance Measure Example Face Verification Gabor Functions Iris Verification Handwriting Verification Genetic Fingerprint Biometric Standards Future of Biometrics. EER - Example.

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Biometrics II

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  1. Biometrics II • Performance Measure Example • Face Verification • Gabor Functions • Iris Verification • Handwriting Verification • Genetic Fingerprint • Biometric Standards • Future of Biometrics

  2. EER - Example Testing a biometric system for various thresholds: Each time, 100 tests were performed, by comparing two patterns. In 40 cases, the two patterns came from the same person, in 60 cases from different persons. The table shows the result. TA: True Acceptance TR: True Rejection FA: False Acceptance FR: False Rejection

  3. Determine Equal-Error-Rate Best threshold is about 37%, EER 29%.

  4. Weighted FAR/FRR • however, now we think that a false rejection is more worse than a false acceptance • reason: it is less likely to have an intruder • we can give weights to the rates • example: a false rejection we weight with 0.8, a false acceptance with 0.2 • to get the new equal-error-rate, we multiply both curves with the weights

  5. Weighted EER times 0.8 times 0.2 Now, best threshold is about 18%, EER 10%.

  6. Receiver Operator Characteristic (ROC)

  7. ROC: Model Cases • ROC is a model for the statistical distribution of correct and wrong answers of a system, depending on a parameter (here: threshold) • in worst case, both distributions overlap completely • in typical case, there is some overlap • in best case, there is no overlap • we monitor, how TAR and FAR grow together, when the parameter goes from 0 to a maximum value • both start at (0%,0%) and end at (100%,100%) • in worst case, both rates are always the same • in best case, first TAR goes from 0 to 100, then, while TAR is 100, FAR goes from 0 to 100 (all in %)

  8. Receiver Operator Characteristic best case worst case our example

  9. From finger to faces For fingerprint verification, the so-called minutiae played a major role. Is there something similar for faces?

  10. Challenges for Face Processing

  11. Face geometry The minutiae in faces are so-called fiducial points (like position of eyes, nose, mouth). Complex image processing algorithms are able to find these points in face images with some success, thus giving the means for a biometric template.

  12. Face geometry: Problems • Each detection is prone to failures. • Strong requirements on the conditions during capture of the face image (illumination, background, head pose, face expression, obstacles like glasses, coverage by hair...). • Detections may depend on each other, and the processing time might be high. • The definition of such a position is not precise (which point is the nose?).

  13. Another one... Eigenfaces Eigen-faces: an image is considered to be a point in a (very!) high-dimensional vector-space, with each pixel position giving an independent direction, and the intensity at this point the coordinate. Then it is assumed that the set of all face images is a linear subspace of the vector-space of all images, and that there is a corresponding projection operator. (btw later this was shown to be wrong) This projection is a linear operator – thus, it has eigen-vectors. The Eigen-vectors are derived for a set of training images, and any new face image is mapped into this sub-space. This gives some components, from which the correspondence of the new face image to the known ones can be calculated.

  14. Eigenfaces A typical set of basis vectors in Eigenface-space, and a decomposition of a new face by such components.

  15. Eigenfaces: Problems • Better suited to identification than verification. • Very high demands on the face image capture, including a correct face aspect. • Performance rather poor, and can be hardly improved – but there are many works, Fisher faces asf. • Such a face subspace does not really exist. • Not much specificity for faces – any image class (cars or so) can be used as well. • Last not least: there was some political pressure to make this method “famous”...

  16. Face mesh: 3rd approach • For the template, a mesh is adapted to the surface of the face. • Later on, the “effort” is measured to make this mesh matching with the newly presented face. • This method is also suitable for 2D images. • Problems are obvious, esp. the imprecision in the placement, and the high computational effort.

  17. Iris: Part of a face! In 2002, a journalist of Geo journal tried to find out about the fate of a girl from Afghanistan, shown on the cover page of the June 1985 issue. He succeeded and could prove the identity of Sharbat Gula. Left: Cover 1985, Sharbat Gula at the age of 17. Right: Herself, 2002. http://magma.nationalgeographic.com/ngm/afghangirl/

  18. The persistence of iris pattern. For this proof, Daugman's iris verification algorithm was used.

  19. Iris recognition Derivation of a specific bit-pattern from the iris image.

  20. Iris recognition: Unwrapping

  21. Iris Pattern: Using Gabor wavelets A 2D-Gabor function is the product of a sine wave and a Gaussian.

  22. Gabor filter Taking various frequencies and orientations, a set of such Gabor functions can be used as a base. The iris pattern is decomposed into such a base, giving a binary pattern. This pattern can be used as a biometric template.

  23. Iris Code Decomposition by Gabor wavelets. Iris code.

  24. Handwriting? Handwriting examination is still posing a big challenge to computer science. In this case, not just the values of features of a handwriting are person-specific, but also the existing features itself.

  25. Dynamic signature verification However, the dynamic pattern of a handwriting (esp. the signature) is suitable for a stable biometric feature that can be used as a biometric template. Such a pattern contains the time functions of pen position, pressure and tilt. A classical method like Dynamical Time Warping (aka Dynamic Programming) can be used for matching.

  26. DNA Fingerprint I • PCR method developed by Kary Mullis in 1983 gave a great stimulus to the field of DNA analysis • Not just for forensic cases, DNA fingerprint is also used for parental tests, detection of inherited diseases, cloning, e.g. of fossile DNA, and derivation of gender (young chicken etc.)

  27. Structure of DNA DNA: CGATTTGA...ATTG .... ATGATG...ATGATG.... GCTTAGACCT “Junk-DNA” - also contains some repeating sequences. The number of repetitions is person-specific (can change with age). Genetic code, only 1-2% of whole DNA.

  28. DNA Fingerprint roughly: primer primer DNA: CGATTTGA...ATTG .... ATGATG...ATGATG.... GCTTAGACCT Polymerase Chain Reaction (PCR): simulates the doubling of DNA strands. Primer cut off a part of the DNA, and DNA polymerase repeatedly force a doubling of the DNA. Once there is sufficient material, its molecular weight is derived with the Agarose gel electrophoresis method.

  29. Genetic vs. real fingerprint • The genetic code is composed of 8 numbers only – but it is not a human id. It can only be used for comparison. • Relatives have similar genetic codes, the GC of twins is identical. • Estimation is fast today, but still some effort and costs. • Proper treatment of the sample is very important. There is an estimated invalidity of about 1% of all tests due to wrong treatment. • In smaller community, probability of the same pattern may go down to 1:30000.

  30. Biometric Standards

  31. Why Biometric Standards? • needed for application development • integration of biometrics in other products • vendor-independent solutions • comparability among different providers of biometric solutions • global use of biometric security techniques (e.g. passport control at the airport) • storing data in biometric databases in standard format • reproducing the origin of a biometric template • protection against forged data

  32. Who makes standards? • proposal of groups from industry • NIST: National Institute of Standards and Technology • ANSI: American National Standards Institute • ISO: International Standard Organization • OASIS: Organization for the Advancement of Structured Information Standards • also: IEEE, ICAO (Intl. Civil Aviation Organization) • national: DIN (Deutsche Industrienorm)

  33. CBEFF: Common Biometrics Exchange Formats Framework NISTIR 6529-A, 2004

  34. Example SBH:Biometric Type • No Information Given ‘000000’ • Multiple Biometrics Used '000001' • Facial Features '000002' • Voice '000004' • Fingerprint '000008' • Iris '000010'' • Retina '000020' • Hand Geometry '000040' • Signature Dynamics '000080' • Keystroke Dynamics '000100' • Lip Movement '000200' • Thermal Face Image '000400' • Thermal Hand Image '000800' • Gait '001000' • Body Odor '002000' • DNA '004000' • Ear Shape '008000' • Finger Geometry '010000' • Palm Print '020000' • Vein Pattern '040000' • Foot Print ‘080000’

  35. BioAPI

  36. BioAPI • joint standard activity of ISO and the International Electrotechnical Commission (IEC) under their Joint Technical Committee 1 (JTC1), Subcommittee SC37 Biometrics • officially called ISO/IEC BioAPI 19784-1, from 1 May 2006 • inofficially called BioAPI 2.0 (2.1 also exists) • initiated by the BioAPI consortium (BioAPI 1.0 and 1.1)

  37. What is specified in BioAPI • modular architecture and interfaces for integration of biometric solutions • given by header declarations in computer language C • modules for: • software for capturing devices • support of image processing, feature extraction etc. • compression • archiving • retrieval • application modi: • personal use • enrollment check • physical access • support multiple biometrics and "telebiometrics"

  38. Example BIR: Biometric Information Record, inherits the structure of CBEFF

  39. The Future of Biometrics An answer from the cinema...

  40. Biometrics in the media: Star Trek Inventions Communicator Tricorder Sliding Door Wireless Earpiece Biometrics Portable Memory Tablet & Stylus Personal Computer GPS

  41. Fingerprints don't lie (1951): fake fingerprints The Mad Magician (1954): fingerprint identification (?) 2001: A Space Odyssey (1968): voice recognition Diamonds Are Forever (1971): fake fingerprint Blade Runner (1982): reading the eye to detect replicant Star Trek II The Wrath of Khan (1982): retinal recognition to open the Genesis project file. Never say never again (James Bond, 1983): Eyeball replacement to access nuclear weapon. Star Trek III The Search for Spock (1984): Kirk voice identification to engage the Enterprise auto-destruction. Beverly Hills Cop II (1987): superglue fumes for latent print Licence To Kill (1989): gun with palm recognition Back to the Future II (1989): fingerprint reader to enter McFly house, Biff paying the taxi. (La Femme) Nikita (1990): fingerprint match. Judge Dredd (1995): biometrically protected gun. Seven (1995): fingerprints. Barb Wire (1996): retinal scan. Mission Impossible (1996): fingerprint, voiceprint, retinal scan. Critical decision (1996): terrorist recognized using voiceprint. Air Force One (1997) Alien 4 Resurrection (1997): breath recognition, which is further spoofed with spray. Face/Off (1997): voice recognition, fingerprint protected jail. Gattaca (1997): DNA analysis. Men in Black (1997): removing fingerprints to become anonymous. Tomorrow never dies (1997): face recognition, fingerprint protected safes. The Creeps (1997): latent fingerprint. Antitrust (2000): dust for fingerprints on the daycare computer keyboard. Charlie’s Angels (2000): iris, fingerprint protected safe, voice-identification software. Dracula 2000 (2000): handprint, voice & eye scan spoofed Hollow Man (2000): fingerprint + voiceprint. Mission Impossible 2 (2000): retinal, facial & voice recognition. X-Men (2000): eye scanned access control. Frequency (2000): fingerprint. Planet of the Apes (2001) hand recognition to access the Oberon control room. Ocean's Eleven (2001): fingerprint access control of the Bellagio safe. Wasabi (2001): handwritten signature. Replicant (2001): face recognition access control. Bad Company (2002): laptop retina protected. Minority report (2002): iris recognition, eyeball replacement. The Bourne Identity (2002): palm-print identification to access safe. X-Men 2 (2003): eye scan, hand access, voice check. Paycheck (2004): face recognition to select the closest candidate. The Bourne Supremacy (2004): HP iPAQ h5000 series / fingerprint. I, Robot (2004): voice id, side palm access control. Catwoman (2004): lips matching. Mr & Ms Smith (2005): Voice recognition to access Mr Smith's computer. The Island (2005): face & fingerprint recognition of a clone. xXx: State of the Union (2005): hand recognition of the president. Les chevaliers du ciel (2005): fingerprint recognition. Ocean's Thirteen (2007) Fingerprint recognition (FingerChip) to access the Greco computer room. Fantastic Four: Rise of the Silver Surfer (2007) hand and fingerprint recognition to access labs. Biometrics in the Movies http://pagesperso-orange.fr/fingerchip/biometrics/movies.htm

  42. Vision of Biometrics • they mostly show a realistic version of biometrics, and not so much “science fiction” (exception “Gattaca”) • the purpose is the same as in reality: access restriction to physical spaces, identification of persons, securing, control of persons etc. • actually, not much innovations are shown, rather reference to an “archetype” is given • neutrality: it is used by the good guys as well as by the bad guys

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