2. 1 Biometrics and the Department of Defense February 17, 2003

3. 2 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.

4. 3 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

5. 4 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

6. 5 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).

7. 6 What is Biometrics? Two key pieces to Biometrics: –Enrollment –Matching (Verification or Identification)

8. 7 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.

9. 8 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

10. 9 Testing Fingerprint Sensors The most common Biometrics used is Fingerprinting. There are two main types of fingerprint sensors. –Capacitive –Optical

11. 10 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.

12. 11 Simulating Environmental Conditions Dry – Baby powder Hot – Heating Pad Cold – Ice Dirty – Dirt Oily – Motor Oil

13. 12 Other Data Collected Sex Age Normal Fingerprint Sample

14. 13 Statistical Methods Used T-test (both one tailed and two tailed) Correlation

15. 14 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?

16. 15 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

17. 16 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

18. 17 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

19. 18 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

20. 19 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

21. 20 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

22. 21 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

23. 22 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

24. 23 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