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Biometric Authentication : using fingerprints and evaluating fingerprint readers

Biometric Authentication : using fingerprints and evaluating fingerprint readers. M. Ndlangisa Supervised by: Prof P. Wentworth and J. Ebden. Why Fingerprints:. Universal Uniqueness/distinctiveness Permanence Measurability/collectability. Project Goals. versus. Background:

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Biometric Authentication : using fingerprints and evaluating fingerprint readers

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  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. Scanner Precise Biometrics 100 SC Digital Persona reader Difference in performance Braae Laboratory[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% +braae 5.02% +braae Experiment Results:Enrolment Success Rate • Enrolment results [1] The CS undergrad Laboratory where the experiment was conducted

  13. 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%+far 30.2%+frr Matching Accuracy: “Binned example” • Verification Success Rate

  14. Precise 100 SC Digital Persona Difference Verification Success Rate 66% 94% 28%+persona FAR 4% 0% 4%+precise Difference 62%+vsr 94%+vsr Matching Accuracy: One-to-one

  15. Binned Example Digital Persona Precise 100 SC Difference in time Average time 3 seconds 1 seconds 2 seconds +persona One-to-one verification 0.5 seconds 0.4 seconds 0.1seconds +persona Difference in time 2.5 seconds +binned 0.6 seconds +binned Access speed: • The average bin size was 9 records 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 BiometricAuthentication will get me an honours degree!!!

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