1. Biometric Authentication : using fingerprints and evaluating fingerprint readers M. Ndlangisa Supervised by: Prof P. Wentworth and J. Ebden

2. Why Fingerprints: Universal Uniqueness/distinctiveness Permanence Measurability/collectability

3. Project Goals versus Background: Investigate the low level functioning of Fingerprint readers Primary: Design an evaluation experiment, Develop simple Software prototype to carry an evaluation and then carry the experiment

4. Overview: Evaluation Experiment Design Software Design and Implementations Experiment results and observations Conclusion

5. Experiment design: Critical evaluation factors -Failure to enroll rate - the number of fingerprints that are rejected by the system -Matching Accuracy( verification success rate) - FAR and FRR -Access speed - time spent on matching -Effects of increasing record size - scalability

6. Experiment design continued………. Experiment data collection -Source -CS 2 students –Braae labs - Volunteers – Calnet -Data recorded - Failed enrolments - false acceptances and false rejects - registration times - verification times - bin size

7. Software Design: The ActiveX control Registration Software Verification Software - one-to-one verification match - one-to-many positive identification match - a hybrid system that partitions the database

8. Registration Software: Latent prints -Before a new registration is made the left- over prints are flushed using a built-in flush command Registration Demonstration using Digital Persona U are U model

9. Verification Software: Three different programs were developed Focus is on the “binned” solution A brief look on the one-to-one program

10. Binned system design:

11. Demonstration: Using digital Persona U are U fingerprint reader

12. Experiment Results : Enrolment Success Rate Enrolment results Scanner Precise Biometrics 100 SC Digital Persona reader Difference in performance Braae Laboratory[1][1] Fail-to-enroll rate 12.05%17.02%4.97% +persona Enrollment-success rate 87.95%82.98 %4.97% +persona Calnet Laboratory Fail-to-enroll rate 5.5%12 %6.5%+persona Enrollment-success rate 94.5%88%6.5%+persona Difference in performance 6.55% +braae5.02% +braae [1][1] The CS undergrad Laboratory where the experiment was conducted

13. Matching Accuracy: “ Binned example” Verification Success Rate Digital Persona U are U Precise Biometrics 100 SC Difference in FRR/FAR FAR 2.74%9.3%6.56%+precise FRR 1.36%39.5%38.14%precise Difference 1.38%+far30.2%+frr

14. Matching Accuracy: One-to-one Precise 100 SC Digital Persona Difference Verification Success Rate 66%94%28%+persona FAR4%0%4%+precise Difference62%+vsr94%+vsr

15. Access speed: The average bin size was 9 records Binned ExampleDigital PersonaPrecise 100 SCDifference in time Average time3 seconds1 seconds2 seconds +persona One-to-one verification 0.5 seconds0.4 seconds0.1seconds +persona Difference in time2.5 seconds +binned0.6 seconds +binned Average time

16. Observations: The Digital persona is better than the Precise fingerprint reader in one important aspect - matching accuracy In general the two fingerprint readers can hardly work unsupervised Making it hard for them to be implemented in a Lab Access Control problem

17. Conclusion: I hope my Affair with Biometric Authentication will get me an honours degree!!!