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Biometrics and theDepartment of Defense
February 17, 2003
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What is Biometrics?
•Biometrics: Traits of the human biological system,suitable for measurement and use in identification.
•There are two type of matching:
–Verification: One to One (involves a token oridentifier).
–Identification: One to Many (used often in forensics).
•One to one is most often used in access controlscenarios.
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What is Biometrics?
• A Few Biometric Applications:
–Prison Visitor Systems
–Drivers license
–Canteen administration
–Benefit payment systems
–Border Control
–Forensics
–Logical Access Control
–Physical Access Control
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What is Biometrics?
•Some Possible Biometrics
–Fingerprint, voice, ear, hand vein, retinal, facial, handgeometry, DNA, keystroke, dental, signature, gait, bodyodor, iris.
•Desirable Biometric Traits
–Universality
–Uniqueness
–Permanence
–Collectible
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What is Biometrics?
•Biometric Performance Terminology
–(FAR) False Accept Rate
–(FRR) False Reject Rate
–Threshold (Sensitivity)
–ROC (Receiver Operating Characteristic) Curve
•This involves plotting FAR and FRR against each otherbetween a varying threshold value.
•Often it is difficult or impossible to change the threshold of a particularvendor’s system.
•Often the biometrics sensor (hardware) is closely tied to the algorithm(enrollment and matching software).
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What is Biometrics?
•Two key pieces to Biometrics:
–Enrollment
–Matching (Verification orIdentification)
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What is Biometrics?
Automated Biometric System:
A system which uses biological,physiological or behavioral characteristicsto automatically authenticate the identity ofan individual based on a previousenrollment.
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Biometrics - Industry Trends
•Sensor Improvements:
–Improved temperature tolerances
–Resistance to Electro-Static Discharge (SD)
–Smaller footprint
–Reduced power consumption
–Additional hardware interfaces available
•Market is Windows-centric
–Expansion into other operating environments
•Sun Solaris, Linux, embedded systems
•Maturation of standards and APIs
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Testing Fingerprint Sensors
•The most common Biometrics used isFingerprinting.
•There are two main types of fingerprintsensors.
–Capacitive
–Optical
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What We Tested
•How do environmental conditions affectfingerprint match scores with a capacitive sensor?The following tools were used:
–Verifinger 4.0 software
–Authentec® capacitive fingerprint (USB)
•Fingerprint samples were colleted from friends,family, and students.
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Simulating EnvironmentalConditions
•Dry – Baby powder
•Hot – Heating Pad
•Cold – Ice
•Dirty – Dirt
•Oily – Motor Oil
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Other Data Collected
•Sex
•Age
•Normal Fingerprint Sample
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Statistical Methods Used
•T-test (both one tailed and twotailed)
•Correlation
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Analysis I
•Two tailed - Is there a relationshipbetween sex and match score?
– Ho: No relationship
– Ha: There is a relationship
•One tailed – Do females receivelower match scores than males?
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Analysis I - Results
•Two tailed – Is there a relationship?
– T – stat |3.54|
–T-critical 1.997
–P – value 0.0003 > 0.05
•One tailed – Is there a relationship?
– T – stat |3.54|
–T-critical 1.6686
–P – value 0.0007 > 0.05
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Analysis II
•Correlation tests
•Whether there is a relationship between anenvironmental condition fingerprint match scoreand the normal fingerprint match score?
•Ho: There is no relationship between the twoscores.
•Ha: There is a relationship between the twoscores
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Analysis II – Normal v . Hot
•P – value (Significance F) = 5.25E-23 >0.05
–Statistical Significance
•Multiple R = 0.8542
–Positive relationship (85.42%) between hotmatch score and normal match score
•Accept Alternative Hypothesis
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Analysis II – Normal v. Cold
•P – value (Significance F) = 7.22E-26 >0.05
–Statistical Significance
•Multiple R = 0.8793
–Positive relationship (87.93%) between coldmatch score and normal match score
•Accept Alternative Hypothesis
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Analysis II – Normal v. Dry
•P – value (Significance F) = 3.21E-07 >0.05
–Statistical Significance
•Multiple R = 0.5438
–Positive relationship (54.38%) between drymatch score and normal match score
•Accept Alternative Hypothesis
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Analysis II – Normal v. Dirty
•P – value (Significance F) = 4.38E-09 >0.05
–Statistical Significance
•Multiple R = 0.6084
–Positive relationship (60.84%) between dirtymatch score and normal match score
•Accept Alternative Hypothesis
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Analysis II – Normal v. Greasy
•P – value (Significance F) = 2.49E-10 >0.05
–Statistical Significance
•Multiple R = 0.6447
–Positive relationship (64.47%) between greasymatch score and normal match score
•Accept Alternative Hypothesis
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Conclusion
•Different entrance threshold ratesshould be used for the different sexes
•Different entrance threshold ratesshould be used for the differentenvironmental conditions
–Dry and Dirty fingers need lower thresholds
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Conclusion
•Biometrics is still an emerging technology.Some more than others.
•The BFC/BMO is providing support andexpertise to aid the the Department ofDefense in the development anddeployment of biometric systems