IJCB 2011A Bayesian Approach to Fingerprint MinutiaLocalization and Quality Assessment using           Adaptable Templates...
Authors   Bradley Department of Electrical and    Computer Engineering     Nathan Short     Lynn Abbott     Michael Hs...
MotivationRolled/Plain Fingerprints               Latent Fingerprints   Large sample of good                    Few good...
Latent vs. Plain/Rolled MinutiaCount*Images from NIST SD27               10/24/2011
Latent vs. Plain/Rolled MinutiaCount               10/24/2011
Motivation (cont.)   Automated Fingerprint Identification System    (AFIS)     Minutia based     Aimed towards Plain/Ro...
Motivation (cont.)              10/24/2011
Motivation (cont.)              10/24/2011
Minutia Localization             10/24/2011
Minutia Localization             10/24/2011
Minutia Localization             10/24/2011
Minutia Localization             10/24/2011
Initial Results                  10/24/2011
Initial Results                  10/24/2011
Initial Results (cont.)   Match Performance (Location)                                                        Mean       ...
Quality    * C. I. Watson, M. D. Garris, E. Tabassi, C. L. Wilson, R. M. McCabe, S. Janet, and K. Ko, (2004)             ...
Initial Results (cont.)   Match Performance (Quality)                     10/24/2011
Initial Results (cont.)               10/24/2011
Initial Results (cont.)   Average and Individual corresponding    minutiae difference                     10/24/2011
Future Work              10/24/2011
Thank you!   Questions?                 10/24/2011
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Ijcb2011 final

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  • Accidental friction ridge skin impression left on a surface (crime scene)Typically not visible, made visible by chemicals like powders ninhydrin then photographed or lifted with adhesive
  • Latent 20.5 (16)Plain 106.3 (80)
  • 99.4% rank-one identification rate (10,000 images) 54% rank-one identification rate (40 mil)
  • Base – 68.7%Best – 67.6%Worst – 55.4%
  • Base – 68.7%Best – 67.6%Worst – 55.4%
  • X_t state Y_t observationAllows for use of prior state estimations when current observation has a high level of noise in low quality regionCondensation algorithm, object tracking and localization
  • - Particle filter
  • MultivariateNormal distribution centered atprevious directionT is an ideal minutia templateS is ROI around m_t^IGives a quality of least squares fitting to original image region
  • d_i,j found by distance transform of skeleton imagedistance to nearest non-zero pixelRange again is [-1,1]Adjust to local mean and variance
  • Ugly group had best improvementTAR 69.3 to 74.6 FAR 30.6 to 25.37
  • T – is number of iterations- Values associated with levels are empirically based
  • TAR increase 95.9 to 97 for goodFAR increase 4.1 to 3.0 for good
  • Test to see if improved locations are not a result of random chanceNon- parametric (independent of distribution of data)Paired difference testT is test statisticAlpha is critical level (95% and 99% confidence interval)-wilcoxon is more definitive
  • 4.55x10-6 for avg. min or ~ 1/350,0003.0 x10-10 for all min pairs or ~ 1/333mil
  • Ijcb2011 final

    1. 1. IJCB 2011A Bayesian Approach to Fingerprint MinutiaLocalization and Quality Assessment using Adaptable Templates Nathan Short, A. Lynn Abbott, Michael S. Hsiao, Edward A. Fox Virginia Tech October 11th, 2011
    2. 2. Authors Bradley Department of Electrical and Computer Engineering  Nathan Short  Lynn Abbott  Michael Hsiao Department of Computer Science  Edward Fox 10/24/2011
    3. 3. MotivationRolled/Plain Fingerprints Latent Fingerprints Large sample of good  Few good quality quality features features for matching  Supervised acquisition of  Low quality sample fingerprint  Low fingerprint surface area 10/24/2011
    4. 4. Latent vs. Plain/Rolled MinutiaCount*Images from NIST SD27 10/24/2011
    5. 5. Latent vs. Plain/Rolled MinutiaCount 10/24/2011
    6. 6. Motivation (cont.) Automated Fingerprint Identification System (AFIS)  Minutia based  Aimed towards Plain/Rolled fingerprint matching  Large sample size Automated Latent fingerprint match performance suffers  Increase quantity of features  Minutiae  Extended features  Improve quality of features  Refine minutia descriptors 10/24/2011
    7. 7. Motivation (cont.) 10/24/2011
    8. 8. Motivation (cont.) 10/24/2011
    9. 9. Minutia Localization 10/24/2011
    10. 10. Minutia Localization 10/24/2011
    11. 11. Minutia Localization 10/24/2011
    12. 12. Minutia Localization 10/24/2011
    13. 13. Initial Results 10/24/2011
    14. 14. Initial Results 10/24/2011
    15. 15. Initial Results (cont.) Match Performance (Location) Mean Print distance % Method % improvement type to ground hit truth NBIS 4.28 8.92 Plain 4.67 Proposed 4.08 15.5 NBIS 4.66 4.99 Latent 6.44 Proposed 4.36 7.07 Mean Print distance % Method % improvement type to ground hit truth NBIS 4.48 6.81 Good 4.70 Proposed 4.27 9.44 NBIS 4.75 3.62 Bad 5.80 Proposed 4.47 6.13 NBIS 4.75 4.65 Ugly 8.21 Proposed 4.36 7.90 10/24/2011
    16. 16. Quality * C. I. Watson, M. D. Garris, E. Tabassi, C. L. Wilson, R. M. McCabe, S. Janet, and K. Ko, (2004) 10/24/2011
    17. 17. Initial Results (cont.) Match Performance (Quality) 10/24/2011
    18. 18. Initial Results (cont.) 10/24/2011
    19. 19. Initial Results (cont.) Average and Individual corresponding minutiae difference 10/24/2011
    20. 20. Future Work 10/24/2011
    21. 21. Thank you! Questions? 10/24/2011

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