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Fingerprint Recognition by Matching of Gabor filter-based Patterns

Fingerprint Recognition by Matching of Gabor filter-based Patterns. Diplomarbeit Aufgabensteller: Prof. Dr. Bernd Radig Betreuer: Dipl. Inf. Matthias Wimmer. Biometrics. Idea: Authentification of human beings using physical characteristics. History of the use of fingerprints:

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Fingerprint Recognition by Matching of Gabor filter-based Patterns

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  1. Fingerprint Recognition by Matching of Gabor filter-based Patterns Diplomarbeit Aufgabensteller: Prof. Dr. Bernd Radig Betreuer: Dipl. Inf. Matthias Wimmer

  2. Biometrics Idea: Authentification of human beings using physical characteristics History of the use of fingerprints: 19th century: Uniqueness of fingerprints 1998: FBI - IAFIS, Integrated Automatic Fingerprint Identification System Technische Universität München Markus Huppmann

  3. Authentification (Workflow) • Enrollment • Detection of unique attributes • Creation of the template • Matching: Comparison of the template with other templates → Matching score → Decision: Acceptance or rejection (threshold) Technische Universität München Markus Huppmann

  4. Minutiae Matching (1) Fingerprint recognition using ridge singularities: - Ridge bifurcation - Ridge ending Technische Universität München Markus Huppmann

  5. Minutiae Matching (2) Technische Universität München Markus Huppmann

  6. Minutiae Matching (3) Matching: → Matchingscore Technische Universität München Markus Huppmann

  7. Problems • Fingerprints of dry or wet fingers • Non-overlapping areas → Global approach: Pattern Matching Technische Universität München Markus Huppmann

  8. Pattern Matching Gabor filter-based Pattern Matching • Normalization • Segmentation • Reference point detection • Gabor filter • Creation of the Feature Map • Matching Technische Universität München Markus Huppmann

  9. Reference Point Detection • Reference point defined as the point, where the ridges possess the highest curvature • Orientation map Technische Universität München Markus Huppmann

  10. Gabor Filter (1) Sinusoid multiplied by a Gaussian function Technische Universität München Markus Huppmann

  11. Gabor Filter (2) Gabor filter in direction 0° Technische Universität München Markus Huppmann

  12. Gabor Filter (3) Technische Universität München Markus Huppmann

  13. Creation of the Feature Map Tessellation → Template Technische Universität München Markus Huppmann

  14. Creation of the Feature Map Technische Universität München Markus Huppmann

  15. Matching (1) Comparison of the feature maps: Similar feature maps → low distance → "good" matching score → acceptance Technische Universität München Markus Huppmann

  16. Matching (2) Technische Universität München Markus Huppmann

  17. Matching (3) Different feature maps → high distance → "bad" matching score → rejection Technische Universität München Markus Huppmann

  18. Matching (4) Technische Universität München Markus Huppmann

  19. Tests • Database of 80 fingers with 4 fingerprints per finger • 2 Tests: • Genuine test: Matching of every fingerprint of the same finger (1A:1B, 1A:1C, 1A:1D, 1B:1C, … , 1C:1D) → "good" matching scores • Imposter test: Matching of the first fingerprint of every set with the first fingerprint of the other sets (1A:2A, 1A:3A, … , 79A:80A) → "bad" matching scores Technische Universität München Markus Huppmann

  20. Biometric benchmarks • FAR: false acceptance rate • FRR: false rejection rate • EER: equal error rate optimal threshold where FAR = FRR Technische Universität München Markus Huppmann

  21. Test results equal error rate = 1.88 % Technische Universität München Markus Huppmann

  22. Questions? Technische Universität München Markus Huppmann

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