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Fingerprint Recognition Through Circular Sampling

Fingerprint Recognition Through Circular Sampling. David Chang* and Joseph Hornak Rochester Institute of Technology Rochester, NY 14623-5604. Introduction. Useful for personal identification. Valuable to criminal investigators and forensic scientists. Overview. Introduction Background

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Fingerprint Recognition Through Circular Sampling

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  1. Fingerprint Recognition Through Circular Sampling David Chang* and Joseph Hornak Rochester Institute of Technology Rochester, NY 14623-5604

  2. Introduction • Useful for personal identification. • Valuable to criminal investigators and forensic scientists.

  3. Overview • Introduction • Background • Theory • Methods • Results • Conclusion • Future Work

  4. Background • Fingerprint Uniqueness • Obstacles • AFIS Method

  5. Fingerprint Uniqueness • Major: Central pattern • arch, loop, whorl

  6. Fingerprint Uniqueness • Minor: Minutiae • ridge termination, bifurcation

  7. Obstacles • Rotation • Displacement • Missing area • Image defects

  8. AFIS Method • Image enhancement • Feature extraction • Feature mapping • Classification via flow maps • Matching • # of minutiae • Euclidean distances

  9. Source Image Circle Sample Theory: Sampling Process

  10. Theory: Sampling Process Fingerprint Image Concentric Circle Sample

  11. Circular Correlation x Rotated x

  12. A = 8 A = 16 MArea Ratio = 0.5 Match Metric: Area Ratio • Average of ratio between the areas of corresponding circles in the two samples being matched.

  13. A = 8 A = 16 MAX = 6 MCorrelation Fraction = 0.75 Match Metric: Correlation Fraction • Average of the max value in the correlated signal divided by smaller area of corresponding circles in the two samples being matched.

  14. Low Match Probability High Match Probability Match Metric: Angular Density • Determine mean square error (MSE) among angles corresponding to highest magnitude in the correlation signal. MAngular Density = 1 - 2(MSE)/

  15. Methods • Source Images • 48 Synthetic Fingerprint Images • 512 x 512 pixels at 1-bit/pixel • Match Matrices • 48 x 48 matrix where each column sample is matched against each row source sample. • Done for the 3 metrics. • Observe effects • Missing Areas • Rotation • Examine displacement effects

  16. Area Ratio Correlation Fraction Angular Density Results: Unchanged Variables

  17. Fingerprint Image Quarter Area Removed Arbitrary Area Removed

  18. Area Ratio Correlation Fraction Angular Density Results: Arbitrary Area Removed

  19. Area Ratio Correlation Fraction Angular Density Results: Rotation Effects Column images rotated 45

  20. MArea Ratio MCorrelation Fraction MAngular Density Arch 16 0 -16 16 0 -16 16 0 -16 -16 0 16 -16 0 16 -16 0 16 Loop 16 0 -16 16 0 -16 16 0 -16 -16 0 16 -16 0 16 -16 0 16 Whorl 16 0 -16 16 0 -16 16 0 -16 -16 0 16 -16 0 16 -16 0 16 Results: Displacement Effects

  21. Conclusion • Of the three metrics, the angular density metric proves to be most effective. • Displacement effects show that a consistent selection of the circles center is necessary.

  22. Future Work • Image enhancement • Test on actual fingerprints • Observe effects of less circles • Test on larger database • Code optimization

  23. Thanks!

  24. Fingerprint Chicks • Materials: • Yellow • Tempera paint • Wash tubs • Large construction paper • Glue • Directions: • 1.Each student chooses a construction paper for background. • 2.Have students come up one at a time, to gently dip their hands and fingers in the yellow tempera paint. • 3.Each will place their hands and fingers on their paper, making a fingerprint. • 4.Kids decorate their fingerprints to look like a new Spring chick.

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