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Biometrics and the Department 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 or

identifier).

– Identification: One to Many (used often in forensics).

• One to one is most often used in access control scenarios.

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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, hand

geometry, DNA, keystroke, dental, signature, gait, body odor, 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 other

between a varying threshold value.• Often it is difficult or impossible to change the threshold of a particular

vendor’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 or Identification)

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What is Biometrics?

Automated Biometric System:

A system which uses biological, physiological or behavioral characteristics to automatically authenticate the identity of an individual based on a previous enrollment.

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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 is Fingerprinting.

• There are two main types of fingerprint sensors.– Capacitive

– Optical

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What We Tested

• How do environmental conditions affect fingerprint 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 Environmental Conditions

• 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 two tailed)

• Correlation

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Analysis I

• Two tailed - Is there a relationship between sex and match score? – Ho: No relationship– Ha: There is a relationship

• One tailed – Do females receive lower 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 an

environmental condition fingerprint match score and the normal fingerprint match score?

• Ho: There is no relationship between the two scores.

• Ha: There is a relationship between the two scores

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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 hot

match 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 cold

match 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 dry

match 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 dirty

match 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 greasy

match score and normal match score

• Accept Alternative Hypothesis

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Conclusion

• Different entrance threshold rates should be used for the different sexes

• Different entrance threshold rates should be used for the different environmental 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 and expertise to aid the the Department of Defense in the development and deployment of biometric systems