# Biometrics II Performance Measure Example Face Verification

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

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

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

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

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

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 %)

best case worst case our example

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

Challenges for Face Processing

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.

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?).

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.

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

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”...

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.

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.

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

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

Iris recognition: Unwrapping

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

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.

Iris Code Decomposition by Gabor wavelets. Iris code.

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.

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.

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.)

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.

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.

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.

Biometric Standards

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

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)

CBEFF: Common Biometrics Exchange Formats Framework
NISTIR 6529-A, 2004 (Standard Biometric Header) SBH SBH Security Options Integrity Options CBEFF Header Version Patron Header Version Biometric Type Biometric Subtype Biometric Data Type Biometric Purpose Biometric Data Quality Biom. Creation Date Validity Period Creator Index (e.g. in database) ... (Biometric Data Block) BDB biometric template if needed, data can be structured optional parameters optional encrypted can follow a different sub-standard (Signature Block) SB depends on SBH Security Options MAC: Message Authentication Code, or: Digital Signature

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’

BioAPI

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 , from 1 May 2006 inofficially called BioAPI 2.0 (2.1 also exists) initiated by the BioAPI consortium (BioAPI 1.0 and 1.1)

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"

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

The Future of Biometrics

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

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

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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