week1 new bio metrics
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8/8/2019 Week1 New Bio Metrics
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Biometrics
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Topics Biometric identifier classification
Biometric identifier characteristics comparison
Multimodal Biometrics
Biometric Standards
Challenges in Biometrics
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Identifiable biometric
characteristics Biological traces
DNA, blood, saliva, etc.
Biological (physiological) characteristics
fingerprints, eye irises and retinas, hand palmsand geometry, and facial geometry
Behavioral characteristics
signature, gait , keystroke dynamics, lip motion, voice
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Example of banking application
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Biometric identifiers
Courtesy of G. Bromba
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Biometric Market Share
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Comparison of biometric
techniques
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Palm
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Hand vein
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Facial Thermogram
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Ear print
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Retina
Human eye has its own totally unique pattern of blood vessels.
Because of its internal location, the retina is protected from variationscaused by exposure to the external environment (unlike fingerprints).
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Which Biometric is the Best? Universality (everyone should have this trait)
Uniqueness (everyone has a different value)
Permanence (should be invariant with time) Collectability (can be measured quantitatively)
Performance (achievable recognition accuracy, resources required, operating environment)
Acceptability (are people willing to accept it?)
Circumvention (how easily can it be spoofed?)
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Selecting a Biometric
Selecting the right biometric is a complicatedproblem that involves more factors than just accuracy. It depends on cost , error rates, computational speed, acquitability, privacy and easyof use.
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Ideal Biometric
CharacteristicsThe ideal biometric characteristics have five qualities:
Robust: Unchanging on an individual over time.
Distinctive: Showing great variation over the population.
Available: The entire population should ideally have thismeasure in multiples.
Accessible: Easy to image using electronic sensors.
Acceptable: People do not object to having this measurement taken on them.
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Quantitative measuresQuantitative measures of these five qualities have been developed.
"Robustness" is measured by the "false non-match rate" (Type I error), the probability that a submitted sample will not match
the enrollment image. "Distinctiveness" is measured by the "false match rate" (Type II
error), the probability that a submitted sample will match theenrollment image of another user.
" Availability" is measured by the "failure to enroll" rate, theprobability that a user will not be able to supply a readable
measure to the system upon enrollment. " Accessibility" can be quantified by the "throughput rate" of the
system, the number of individuals that can be processed in aunit time, such as a minute or an hour.
" Acceptability" is measured by polling the device users.
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Biometric System Goals A biometric system can be designed to test one of only two possible
hypotheses:
The submitted samples are from an individual known to thesystem
The submitted samples are from an individual not known to thesystem
Applications to test the first hypothesis are called "positive
identification" systems while applications testing the latter are
called "negative identification" systems.
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Types of Biometrics Overt Versus Covert: The first partition is "overt/covert ". If the user is aware that a
biometric identifier is being measured, the user is overt. If unaware, the use iscovert. Almost all conceivable access control and non-forensic applications are overt.Forensic applications can be covert.
Habituated Versus Non-Habituated: This applies to the intended users of theapplication. Users presenting a biometric trait on a daily basis can be consideredhabituated after a short period of time. Users who have not presented the trait recently can be considered "non-habituated".
Attended Versus Non- Attended: This partition refers to whether the use of thebiometric device during operation will be observed and guided by system
management.
Open Versus Closed: If a system is to be open, data collection, compression andformat standards are required. A closed system can operate perfectly well oncompletely proprietary formats.
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Generic Biometric System
A generic biometric system.
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Multimodal Biometrics Multimodal Biometric system is a system
that uses more than one independent or
weakly correlated biometric identifier takenfrom an individual (e.g., fingerprint and faceof the same person, or fingerprints from twodifferent fingers of a person)
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Multi-modal Systems: Fusion Early integration or sensor fusion
Integration is performed on the feature level
Classification is done on the combinedfeature vector
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Multi-modal Systems: FusionLate integration or decision fusion
Each modality is first pre-classified independently
The final classification is based on the fusion of the outputs of the different modalities
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Multimodal biometrics
systems Multimodal biometrics systems improve performance
A combination in a verification system improvessystem accuracy
A combination in an identification system improvessystem speed as well as accuracy
A combination of uncorrelated modalities (e.g.fingerprint and face, two fingers of a person, etc.) is
expected to result in a better improvement inperformance than a combination of correlatedmodalities (e.g. different fingerprint matchers)
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Other work: classification FBI Fingerprint card (includes information on
gender, ethnicity, height , weight , eye color
and hair color) Wayman (1997) proposed filtering large
biometric databases based on gender andage
Givens et al. (2003) and Newham (1995)showed that age, gender and ethnicity canaffect the performance of a biometric system
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International Standards
Bodies
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Application Programming
Interface ( API) Biometrics is the automated use of
physiological or behavioral characteristics to
determine or verify an identity Standards for interfaces and methods for
performance evaluation are needed
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Biometric Authentication
Systems Layers of interaction with biometric authentication
systems
Scope Standardization of generic biometric technologies to
support interoperability and data interchange between
applications and systems Included: common file formats, application programming
interfaces ( APIs), biometric templates, template protectiontechniques, related application/implementation profiles, methodologies for conformity
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Basic Standards Bio API ± The most popular API in the
biometrics area
CBEFF ±
Common Biometric Exchange FileFormat
ANSI X9.84-2003 ± Biometric InformationManagement and Security for the Financial
Services Industry ISO/IEC 19794 ± Biometric DataInterchange Formats
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Challenges in Biometrics Large number of classes (~ 6 billion faces)
Large intra-class variability
Small inter-class variability Segmentation
Noisy and distorted images
Population coverage & scalability
System performance (error rate, speed, cost)
Attacks on the biometric system
Every biometric characteristic has some limitations
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Threats to Biometrics
The Modern Burglar
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Matsumoto¶s Technique
Only a few dollars¶ worth of materials
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Making the Actual Clone
You can place the ³gummy finger´ over your realfinger. Observers aren¶t likely to detect it when youuse it on a fingerprint reader.
Don¶t try this at home! (Matsumoto)
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Summary There is wide variety of biometric identifiers that
posses different characteristics
Each biometric system should take into account theend goal of application
Multi-biometrics improve performance of individualmatchers and is active topic of current biometricresearch
Biometric standards are being developed, whilebiometric reliability is still a concern
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Reference and Links Signal Processing Institute, Swiss FederalInstitute of Technology
http://scgwww.epfl.ch/ Biometric Systems Lab, University of Bologna
http://bias.csr.unibo.it/research/biolab/
www.sciencedierect.com Textbooks 1 and 2 CPSC 601.20
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